diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..37aeb4aea9aa9fad10c30e7cd93d13fb22794063
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.ipynb_checkpoints
+project_dataset
+models
+outputs
diff --git a/.ipynb_checkpoints/Machine_learning_Phase_2 (1)-checkpoint.ipynb b/.ipynb_checkpoints/Machine_learning_Phase_2 (1)-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..9b64047cc39d431f849dbb8829d624c57c59da41
--- /dev/null
+++ b/.ipynb_checkpoints/Machine_learning_Phase_2 (1)-checkpoint.ipynb
@@ -0,0 +1,1395 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9f24d7b3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from sklearn import svm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "6db6f763",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import seaborn as sns\n",
+ "from imblearn.over_sampling import SMOTE\n",
+ "from numpy.random import RandomState\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
+ "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict, StratifiedKFold\n",
+ "\n",
+ "%matplotlib inline\n",
+ "random_seed = 63445"
+ ]
+ },
+ {
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+ "execution_count": 43,
+ "id": "be6f26bb",
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+ "1210588 0.0 0.0 0.0 0.0 \n",
+ "1210589 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ "[1210590 rows x 136 columns]"
+ ]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "dn = pd.read_csv('project_dataset/partial_dataset_train/complete_labels.csv', sep=',', header=0)\n",
+ "dn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "id": "3cb70898",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Unnamed: 0\n",
+ "AGX32190.1 1\n",
+ "AGX32191.1 1\n",
+ "AGX32192.1 1\n",
+ "AGX32193.1 1\n",
+ "AGX32194.1 1\n",
+ " ..\n",
+ "WP_210399566.1 1\n",
+ "WP_210399567.1 1\n",
+ "WP_210399568.1 1\n",
+ "WP_210399569.1 1\n",
+ "WP_210399570.1 1\n",
+ "Length: 1200274, dtype: int64"
+ ]
+ },
+ "execution_count": 103,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tmp = dn.pivot_table(columns=[\"Unnamed: 0\"], aggfunc=\"size\")\n",
+ "# dn[dn[\"Unnamed: 0\"] == \"WP_001181005.1\n",
+ "tmp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "id": "6ea48a16",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "42304 (21210, 134) (21210, 134)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "1210590"
+ ]
+ },
+ "execution_count": 119,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# del df, dm, dn, dl\n",
+ "tmp = dn[dn[\"label\"] == True].iloc[:,2:]\n",
+ "tmp1 = tmp[(tmp.select_dtypes(include=[\"number\"]) != 0).any(1)]\n",
+ "print(np.count_nonzero(tmp1), tmp.shape, tmp1.shape)\n",
+ "# for i in range(10):\n",
+ "# plt.figure()\n",
+ "# plt.spy(tmp1.iloc[i*100:(i+1)*100, :])\n",
+ "# plt.show()\n",
+ "\n",
+ "# group_by_pf = dn.groupby(by=['Unnamed: 0']) \n",
+ "\n",
+ "# df2 = pd.DataFrame()\n",
+ "# group_by_pf = df2.assign(protein_fam=dn[\"Unnamed: 0\"], protein_id=dl[\"Unnamed: 0\"]).groupby([\"protein_fam\"])\n",
+ "# group_by_pf.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0c73a0d",
+ "metadata": {},
+ "source": [
+ "## Defining metrics"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "5792624a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def metrics(true, preds):\n",
+ " accuracy = accuracy_score(true, preds)\n",
+ " recall = recall_score(true, preds)\n",
+ " precision = precision_score(true, preds)\n",
+ " f1score = f1_score(true, preds)\n",
+ " print ('accuracy: {}, recall: {}, precision: {}, f1-score: {}'.format(accuracy, recall, precision, f1score))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "74442bc0",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9057ca37",
+ "metadata": {},
+ "source": [
+ "## 1. Implement a cross-validation strategy :"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "id": "13f72782",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#X = pd.read_csv('partial_dataset_train\\\\features.csv', index_col=0)\n",
+ "#y = pd.read_csv('partial_dataset_train\\\\labels.csv', index_col=0)\n",
+ "\n",
+ "X = pd.read_csv(\"project_dataset/partial_dataset_train/features.csv\", index_col=0)\n",
+ "X = X[:30000]\n",
+ "\n",
+ "y = pd.read_csv(\"project_dataset/partial_dataset_train/labels.csv\", index_col=0)\n",
+ "y = y[:30000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 69,
+ "id": "08b8e5e3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<BarContainer object of 2 artists>"
+ ]
+ },
+ "execution_count": 69,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "Labels = y[\"label\"].values.flatten()\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.bar([\"pos\", \"neg\"], [Labels.sum(), (~Labels).sum()])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "6e9fef85",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train data shape: (21000, 952)\n",
+ "Test data shape: (9000, 952)\n"
+ ]
+ }
+ ],
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.30, random_state=random_seed, stratify=y)\n",
+ "print (\"Train data shape: \", X_train.shape)\n",
+ "print (\"Test data shape: \", X_test.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "7312feb3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_train=y_train.values.flatten()\n",
+ "y_test=y_test.values.flatten().shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "9020e99b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kf = StratifiedKFold(n_splits=5, random_state=None)\n",
+ "cross_val_f1_score_lst = []\n",
+ "cross_val_accuracy_lst = []\n",
+ "cross_val_recall_lst = []\n",
+ "cross_val_precision_lst = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 70,
+ "id": "615988d3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "validation = pd.read_csv('partial_dataset_valid\\\\features.csv', index_col=0)\n",
+ "validation = validation[:10000]\n",
+ "target_val = pd.read_csv('partial_dataset_valid\\\\labels.csv', index_col=0)\n",
+ "target_val = target_val[:10000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "2b70cdea",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(41446, 952) (41446, 1)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Local\\Temp\\ipykernel_32888\\1302110072.py:6: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " rf.fit(X_train_res, y_train_res)\n",
+ "C:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
+ " _warn_prf(average, modifier, msg_start, len(result))\n"
+ ]
+ }
+ ],
+ "source": [
+ "# keeping validation set apart and oversampling in each iteration using smote\n",
+ "sm = SMOTE(random_state=random_seed, n_jobs=-1)\n",
+ "X_train_res, y_train_res = sm.fit_resample(X_train, y_train)\n",
+ "print (X_train_res.shape, y_train_res.shape)\n",
+ " # training the model on oversampled 4 folds of training set\n",
+ "rf = RandomForestClassifier(n_estimators=5, random_state=random_seed)\n",
+ "rf.fit(X_train_res, y_train_res)\n",
+ " # testing on 1 fold of validation set\n",
+ "validation_preds = rf.predict(validation)\n",
+ "cross_val_recall_lst.append(recall_score(target_val, validation_preds))\n",
+ "cross_val_accuracy_lst.append(accuracy_score(target_val, validation_preds))\n",
+ "cross_val_precision_lst.append(precision_score(target_val, validation_preds))\n",
+ "cross_val_f1_score_lst.append(f1_score(target_val, validation_preds))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b7de9408",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "7f8235f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Cross validated accuracy: 0.9928763528525404\n",
+ "Cross validated recall score: 0.012012987012987012\n",
+ "Cross validated precision score: 0.03366006728778468\n",
+ "Cross validated f1_score: 0.017458287091001053\n"
+ ]
+ }
+ ],
+ "source": [
+ "print ('Cross validated accuracy: {}'.format(np.mean(cross_val_accuracy_lst)))\n",
+ "print ('Cross validated recall score: {}'.format(np.mean(cross_val_recall_lst)))\n",
+ "print ('Cross validated precision score: {}'.format(np.mean(cross_val_precision_lst)))\n",
+ "print ('Cross validated f1_score: {}'.format(np.mean(cross_val_f1_score_lst)))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "04c34da8",
+ "metadata": {},
+ "source": [
+ "## 2. Creating a machine learning pipeline as a benchmark"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "id": "97e09568",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_ = pd.read_csv(\"partial_dataset_train\\\\features.csv\")\n",
+ "X_ = X_[:30000]\n",
+ "\n",
+ "y_ = pd.read_csv(\"partial_dataset_train\\\\labels.csv\")\n",
+ "y_ = y_[:30000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f0e20c54",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/notebooks/.ipynb_checkpoints/ml-pipelines-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/ml-pipelines-checkpoint.ipynb
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index 0000000000000000000000000000000000000000..5fb672ec67e0a7c5db547acc103a3946cc07aec4
--- /dev/null
+++ b/notebooks/.ipynb_checkpoints/ml-pipelines-checkpoint.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "da350838-0cdd-43c0-b7bb-3e9d5f07a789",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'1.1.3'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import sklearn\n",
+ "sklearn.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d4a009df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "6f348319",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, PowerTransformer, LabelEncoder\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.feature_selection import SelectKBest, SelectPercentile\n",
+ "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, AdaBoostClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+ "from sklearn.model_selection import StratifiedGroupKFold, GridSearchCV\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4445ebe2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: total: 1min 26s\n",
+ "Wall time: 1min 26s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "DATA_DIR = \"../project_dataset\"\n",
+ "TRAIN_DIR = f\"{DATA_DIR}/partial_dataset_train\"\n",
+ "\n",
+ "# first_n = -1\n",
+ "# first_n = 30_000\n",
+ "first_n = 250_000\n",
+ "\n",
+ "X_ = pd.read_csv(f\"{TRAIN_DIR}/features.csv\", index_col=0)\n",
+ "y_ = pd.read_csv(f\"{TRAIN_DIR}/labels.csv\", index_col=0)\n",
+ "cl_df_ = pd.read_csv(f\"{TRAIN_DIR}/complete_labels.csv\", index_col=0)\n",
+ "\n",
+ "if first_n > 0:\n",
+ " X = X_[:first_n]\n",
+ " y = y_[:first_n]\n",
+ " cl_df = cl_df_[:first_n]\n",
+ "else:\n",
+ " X = X_\n",
+ " y = y_\n",
+ " cl_df = cl_df_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c6b074a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if \"group_id\" in cl_df:\n",
+ " cl_df.drop(\"group_id\", axis=1)\n",
+ "\n",
+ "cl_df[\"group_id\"] = cl_df.astype(bool).groupby(cl_df.columns.tolist(), sort=False).ngroup() + 1\n",
+ "min_ = cl_df[\"group_id\"].min()\n",
+ "max_ = cl_df[\"group_id\"].max()\n",
+ "\n",
+ "def f(r):\n",
+ " if r[\"label\"] == False:\n",
+ " r[\"group_id\"] = np.random.randint(min_, max_, size=1)[0]\n",
+ " return r[\"group_id\"]\n",
+ "\n",
+ "group_ids = cl_df[[\"label\", \"group_id\"]].apply(f, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b0edc83b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = X.values#.astype(np.float32)\n",
+ "y = LabelEncoder().fit_transform(y.values.squeeze())#.astype(np.uint8)\n",
+ "groups = group_ids.to_numpy()#.astype(np.uint16)\n",
+ "\n",
+ "del X_, y_, cl_df, cl_df_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d69beef-13c5-44bb-8527-3573f3717375",
+ "metadata": {},
+ "source": [
+ "## Undersampling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "bc3f406a-0b9c-43aa-b101-763ae6929567",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from imblearn.under_sampling import *\n",
+ "from collections import Counter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c089fc66-b52b-461d-a067-c62820563d54",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# undersample = EditedNearestNeighbours(sampling_strategy=\"majority\",\n",
+ "# n_neighbors=11,\n",
+ "# kind_sel=\"all\",\n",
+ "# n_jobs=-1) # 7 < k < 11\n",
+ "\n",
+ "undersample = OneSidedSelection(sampling_strategy=\"majority\", n_neighbors=3, n_seeds_S=100, n_jobs=-1) # 3 < k < 11"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "271da0ac-b017-4ac4-a75b-8d4c31c2c511",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original dataset shape Counter({0: 246238, 1: 3762})\n",
+ "Resampled dataset shape Counter({0: 131670, 1: 3762})\n",
+ "CPU times: total: 1min 44s\n",
+ "Wall time: 9.13 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# undersample = RepeatedEditedNearestNeighbours(sampling_strategy=\"majority\", n_jobs=-1) #not good enough\n",
+ "# undersample = NeighbourhoodCleaningRule(sampling_strategy=\"majority\", n_jobs=-1, threshold_cleaning=0.2)\n",
+ "# undersample = TomekLinks(sampling_strategy=\"majority\", n_jobs=-1) # not much reduction\n",
+ "\n",
+ "# undersample = NearMiss(sampling_strategy=1/35, n_jobs=-1) # ratio of 1/25 to 1/50 works well on valid set\n",
+ "X_res, y_res = undersample.fit_resample(X, y)\n",
+ "print('Original dataset shape %s' % Counter(y))\n",
+ "print('Resampled dataset shape %s' % Counter(y_res))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "68833452-9559-40e0-8971-e3a89fbefde1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = X_res\n",
+ "y = y_res\n",
+ "groups = groups[undersample.sample_indices_]\n",
+ "\n",
+ "del X_res, y_res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6ff74e5d",
+ "metadata": {},
+ "source": [
+ "## Getting groups for each protein in the dataset for KFold"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b0c4322a",
+ "metadata": {},
+ "source": [
+ "## Define K fold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ecb4ecd6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# n_splits = 5\n",
+ "# cv = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7705fc6b",
+ "metadata": {},
+ "source": [
+ "## Building pipes for each ML algorithm\n",
+ "We'll be using grid search. The following code was just a trial on how to use pipes in sklearn."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a455a859",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "n_components = 10\n",
+ "\n",
+ "pipe_lr = Pipeline(steps=[\n",
+ " (\"lr_scaler\", StandardScaler()),\n",
+ " (\"lr_dim_reduce\", PCA(n_components=n_components)),\n",
+ " (\"lr_clf\", LinearRegression(n_jobs=-1))]\n",
+ ")\n",
+ "\n",
+ "pipe_rf = Pipeline(steps=[\n",
+ " (\"rf_scaler\", StandardScaler()),\n",
+ " (\"rf_dim_reduce\", PCA(n_components=n_components)),\n",
+ " (\"rf_clf\", RandomForestClassifier(n_jobs=-1))]\n",
+ ")\n",
+ "\n",
+ "pipe_svm = Pipeline(steps=[\n",
+ " (\"svm_scaler\", StandardScaler()),\n",
+ " (\"svm_dim_reduce\", PCA(n_components=n_components)),\n",
+ " (\"svm_clf\", SVC())]\n",
+ ")\n",
+ "\n",
+ "pipelines = {\n",
+ " \"Linear Regression\": pipe_lr,\n",
+ " \"Random Forest\": pipe_rf,\n",
+ " \"Support Vector Machine\": pipe_svm, \n",
+ "}\n",
+ "\n",
+ "scores = {key: [] for key in pipelines.keys()}\n",
+ "\n",
+ "for train_id, test_id in cv.split(X, y, groups):\n",
+ "\n",
+ " X_train = X[train_id]\n",
+ " y_train = y[train_id]\n",
+ " X_test = X[test_id]\n",
+ " y_test = y[test_id]\n",
+ "\n",
+ " for clf_name, pipe in pipelines.items():\n",
+ "\n",
+ " pipe.fit(X_train, y_train)\n",
+ " s = pipe.score(X_test, y_test)\n",
+ " scores[clf_name].append(round(s, 3))\n",
+ "\n",
+ " print(\"#\", end=\"\")\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "pd.DataFrame(scores)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d898703c",
+ "metadata": {},
+ "source": [
+ "## Combining Cross validation and all the pipes in GridSearchCV\n",
+ "This takes time. ALOT OF TIME! So, only choose the right algorithms and parameters for our problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e07adab8-e89c-4959-9fa1-7b540b403a70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_metrics(y_true, y_pred):\n",
+ " from sklearn.metrics import balanced_accuracy_score, precision_score, roc_auc_score, f1_score\n",
+ " return {\n",
+ " \"b_acc\": round(balanced_accuracy_score(y_true, y_pred), 2),\n",
+ " \"prec\": round(precision_score(y_true, y_pred), 2),\n",
+ " \"f1\": round(f1_score(y_true, y_pred), 2),\n",
+ " \"roc\": round(roc_auc_score(y_true, y_pred), 2)\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "91386743",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " # # Logistic Regression\n",
+ " # {\n",
+ " # \"dim_reduce__n_components\": np.arange(5, 16, 5),\n",
+ " # \"clf\": [LogisticRegression()],\n",
+ " # \"clf__penalty\": [\"l2\"],\n",
+ " # \"clf__C\": np.logspace(0, 4, 5),\n",
+ " # \"clf__solver\": [\"newton-cg\", \"saga\", \"sag\", \"liblinear\"]\n",
+ " # },\n",
+ " # # Random Forests\n",
+ " # {\n",
+ " # \"scaler\": [RobustScaler()],\n",
+ " # \"scaler__unit_variance\": [True, False],\n",
+ " # \"dim_reduce\": [SelectPercentile()],\n",
+ " # \"dim_reduce__percentile\": np.arange(10, 51, 10),\n",
+ " # \"clf\": [RandomForestClassifier()],\n",
+ " # \"clf__n_estimators\": np.arange(100, 201, 50),\n",
+ " # \"clf__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
+ " # \"clf__max_features\": [\"sqrt\", \"log2\"],\n",
+ " # \"clf__class_weight\": [\"balanced\", \"balanced_subsample\"]\n",
+ " # },\n",
+ " # # Support Vector Machine\n",
+ " # {\n",
+ " # \"pca__n_components\": [2, 20],\n",
+ " # \"clf\": [SVC()],\n",
+ " # \"clf__C\": np.logspace(0, 4, 3),\n",
+ " # \"clf__kernel\": [\"poly\"],\n",
+ " # \"clf__degree\": [3],\n",
+ " # \"clf__class_weight\": [None, \"balanced\"],\n",
+ " # }\n",
+ " # \"clf__base_estimator\": [#LogisticRegression(class_weight=\"balanced\",\n",
+ " # #max_iter=1500, C=0.18, solver=\"saga\")],\n",
+ " # SVC(class_weight=\"balanced\", kernel=\"poly\")],\n",
+ " # \"clf__n_estimators\": [30],\n",
+ " # \"clf__learning_rate\": [0.1, 1],\n",
+ " # \"clf__algorithm\": [\"SAMME\", \"SAMME.R\"]\n",
+ "\n",
+ "default_pipe = Pipeline(steps=[\n",
+ " (\"scaler\", PowerTransformer()),\n",
+ " (\"dim_reduce\", PCA(n_components=20)),\n",
+ " (\"clf\", LogisticRegression(class_weight=\"balanced\", max_iter=2500, C=0.18, solver=\"saga\"))\n",
+ "])\n",
+ "\n",
+ "param_grid = [\n",
+ " {\n",
+ " \"clf\": [LogisticRegression(class_weight=\"balanced\", max_iter=1500, C=0.18, solver=\"saga\")],\n",
+ " }\n",
+ "]\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "7731d0e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
+ "CPU times: total: 2min 27s\n",
+ "Wall time: 5min 2s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "run_gridcv = True\n",
+ "n_splits = 5\n",
+ "cv = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)\n",
+ "\n",
+ "\n",
+ "if run_gridcv:\n",
+ " grid_search = GridSearchCV(estimator=default_pipe,\n",
+ " param_grid=param_grid,\n",
+ " cv=cv.split(X, y, groups),\n",
+ " scoring=[\"balanced_accuracy\", \"precision\", \"f1\", \"roc_auc\"],\n",
+ " refit=\"roc_auc\",\n",
+ " error_score=\"raise\",\n",
+ " n_jobs=-1,\n",
+ " verbose=4,\n",
+ " )\n",
+ " grid_clf = grid_search.fit(X, y)\n",
+ "else:\n",
+ " best_bacc = 0\n",
+ " best_roc = 0\n",
+ " best_model = None\n",
+ " mean = {\"b_acc\": 0, \"prec\": 0, \"f1\": 0, \"roc\": 0}\n",
+ " from copy import deepcopy\n",
+ " for i, (train_id, test_id) in enumerate(cv.split(X, y, groups)):\n",
+ "\n",
+ " X_train, X_test = X[train_id], X[test_id]\n",
+ " y_train, y_test = y[train_id], y[test_id]\n",
+ " \n",
+ " model = deepcopy(default_pipe)\n",
+ " model.fit(X_train, y_train)\n",
+ " y_pred = model.predict(X_test)\n",
+ " \n",
+ " tmp = get_metrics(y_test, y_pred)\n",
+ " \n",
+ " print(i+1, end=\"\")\n",
+ " for k, v in tmp.items():\n",
+ " mean[k] += v\n",
+ " print(f\"\\t{k}: {round(v, 3)}\", end=\"\")\n",
+ " print()\n",
+ " \n",
+ " if best_roc < tmp[\"roc\"]:\n",
+ " best_bacc = tmp[\"b_acc\"]\n",
+ " best_roc = tmp[\"roc\"]\n",
+ " best_model = model\n",
+ " else:\n",
+ " del model\n",
+ " print(\"mean\", end=\"\")\n",
+ " for k, v in mean.items():\n",
+ " print(f\"\\t{k}: {round(v/n_splits, 3)}\", end=\"\")\n",
+ " print()\n",
+ " #refit the best model on the entire dataset\n",
+ " best_model.fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "2e3664dc-048f-47d3-af19-f50045c220ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=20)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.18, class_weight='balanced', max_iter=1500,\n",
+ " solver='saga')</pre></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grid_clf.best_estimator_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "6dc4ce7e-4664-495e-9621-6d5c9e10022e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>rank_test_balanced_accuracy</th>\n",
+ " <th>mean_test_balanced_accuracy</th>\n",
+ " <th>mean_test_f1</th>\n",
+ " <th>mean_test_roc_auc</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0.73015</td>\n",
+ " <td>0.143628</td>\n",
+ " <td>0.807796</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " rank_test_balanced_accuracy mean_test_balanced_accuracy mean_test_f1 \\\n",
+ "0 1 0.73015 0.143628 \n",
+ "\n",
+ " mean_test_roc_auc \n",
+ "0 0.807796 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "if run_gridcv:\n",
+ " tmp = pd.DataFrame(grid_clf.cv_results_)\n",
+ "tmp[[\"rank_test_balanced_accuracy\", \"mean_test_balanced_accuracy\", \"mean_test_f1\", \"mean_test_roc_auc\"]].sort_values([\"rank_test_balanced_accuracy\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "854649cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "if run_gridcv:\n",
+ " acc = round(max(grid_clf.cv_results_[\"mean_test_balanced_accuracy\"]), 2)\n",
+ " roc = round(max(grid_clf.cv_results_[\"mean_test_roc_auc\"]), 2)\n",
+ " usample_name = str(undersample.__class__).split(\".\")[-1][:-2]\n",
+ " algo_name = str(grid_clf.best_estimator_.steps[2][1].__class__).split(\".\")[-1][:-2]\n",
+ " filename = f\"../models/{usample_name}_best_model_{first_n}_{algo_name}_{acc}_{roc}.pkl\"\n",
+ " pickle.dump(grid_clf.best_estimator_, open(filename, 'wb'))\n",
+ "else:\n",
+ " acc = round(mean[\"b_acc\"]/n_splits, 2)\n",
+ " roc = round(mean[\"roc\"]/n_splits, 2)\n",
+ " algo_name = str(best_model.steps[2][1].__class__).split(\".\")[-1][:-2]\n",
+ " filename = f\"../models/best_model_{first_n}_{algo_name}_{acc}_{roc}.pkl\"\n",
+ " pickle.dump(best_model, open(filename, 'wb'))\n",
+ " best_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d966f427-46ab-4768-86a7-2eb4a022a672",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/Data_Preprocessing.ipynb b/notebooks/Data_Preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..8d42af292563c335219640f33e948f2365efb81e
--- /dev/null
+++ b/notebooks/Data_Preprocessing.ipynb
@@ -0,0 +1,1294 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "aabd5598",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "from matplotlib import pyplot as plt\n",
+ "import numpy as np\n",
+ "import collections\n",
+ "from collections import Counter\n",
+ "\n",
+ "import sklearn\n",
+ "\n",
+ "from sklearn.preprocessing import StandardScaler,RobustScaler,MinMaxScaler,MaxAbsScaler, PowerTransformer\n",
+ "from sklearn.feature_selection import SelectKBest, chi2\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.datasets import make_blobs\n",
+ "from sklearn.impute import SimpleImputer\n",
+ "from sklearn.compose import ColumnTransformer\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn import decomposition\n",
+ "\n",
+ "from sklearn.neighbors import KNeighborsClassifier\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn import linear_model\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.ensemble import RandomForestClassifier\n",
+ "from sklearn.ensemble import GradientBoostingClassifier\n",
+ "from sklearn.ensemble import AdaBoostClassifier\n",
+ "from sklearn.linear_model import SGDClassifier\n",
+ "\n",
+ "from sklearn.metrics import accuracy_score\n",
+ "from sklearn.metrics import classification_report\n",
+ "\n",
+ "random_state = np.random.RandomState(seed=42)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f954d0c1",
+ "metadata": {},
+ "source": [
+ "### Train data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "dd761b6d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "first_n = 30_000\n",
+ "\n",
+ "X_ = pd.read_csv('partial_dataset_train\\\\features.csv', index_col=0)\n",
+ "X = X_[:first_n]\n",
+ "\n",
+ "y_ = pd.read_csv('partial_dataset_train\\\\labels.csv', index_col=0)\n",
+ "y = y_[:first_n]\n",
+ "\n",
+ "del X_, y_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f64c3eb0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>C0-1</th>\n",
+ " <th>C0-2</th>\n",
+ " <th>C0-3</th>\n",
+ " <th>C0-4</th>\n",
+ " <th>C0-5</th>\n",
+ " <th>C0-6</th>\n",
+ " <th>C0-7</th>\n",
+ " <th>C0-8</th>\n",
+ " <th>C0-9</th>\n",
+ " <th>C0-10</th>\n",
+ " <th>...</th>\n",
+ " <th>C8-91</th>\n",
+ " <th>C8-92</th>\n",
+ " <th>C8-93</th>\n",
+ " <th>C8-94</th>\n",
+ " <th>C8-95</th>\n",
+ " <th>C8-96</th>\n",
+ " <th>C8-97</th>\n",
+ " <th>C8-98</th>\n",
+ " <th>C8-99</th>\n",
+ " <th>C8-100</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>GCA_000474035.1_ASM47403v1.AGX32190.1</th>\n",
+ " <td>0.048</td>\n",
+ " <td>0.143</td>\n",
+ " <td>0.190</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.000</td>\n",
+ " <td>...</td>\n",
+ " <td>3.261</td>\n",
+ " <td>-0.408</td>\n",
+ " <td>-1.416</td>\n",
+ " <td>-2.440</td>\n",
+ " <td>1.106</td>\n",
+ " <td>-0.988</td>\n",
+ " <td>-2.093</td>\n",
+ " <td>0.879</td>\n",
+ " <td>0.848</td>\n",
+ " <td>0.490</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>GCA_000474035.1_ASM47403v1.AGX32191.1</th>\n",
+ " <td>0.079</td>\n",
+ " <td>0.068</td>\n",
+ " <td>0.052</td>\n",
+ " <td>0.082</td>\n",
+ " <td>0.019</td>\n",
+ " <td>0.060</td>\n",
+ " <td>0.025</td>\n",
+ " <td>0.014</td>\n",
+ " <td>0.030</td>\n",
+ " <td>0.088</td>\n",
+ " <td>...</td>\n",
+ " <td>46.974</td>\n",
+ " <td>5.258</td>\n",
+ " <td>-9.220</td>\n",
+ " <td>-38.018</td>\n",
+ " <td>33.755</td>\n",
+ " <td>-14.714</td>\n",
+ " <td>-28.453</td>\n",
+ " <td>9.052</td>\n",
+ " <td>15.603</td>\n",
+ " <td>18.250</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>GCA_000474035.1_ASM47403v1.AGX32192.1</th>\n",
+ " <td>0.119</td>\n",
+ " <td>0.087</td>\n",
+ " <td>0.052</td>\n",
+ " <td>0.100</td>\n",
+ " <td>0.052</td>\n",
+ " <td>0.074</td>\n",
+ " <td>0.032</td>\n",
+ " <td>0.013</td>\n",
+ " <td>0.026</td>\n",
+ " <td>0.042</td>\n",
+ " <td>...</td>\n",
+ " <td>32.604</td>\n",
+ " <td>5.340</td>\n",
+ " <td>2.275</td>\n",
+ " <td>-22.717</td>\n",
+ " <td>26.647</td>\n",
+ " <td>-8.617</td>\n",
+ " <td>-15.550</td>\n",
+ " <td>0.318</td>\n",
+ " <td>7.316</td>\n",
+ " <td>18.674</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>GCA_000474035.1_ASM47403v1.AGX32193.1</th>\n",
+ " <td>0.110</td>\n",
+ " <td>0.065</td>\n",
+ " <td>0.040</td>\n",
+ " <td>0.126</td>\n",
+ " <td>0.054</td>\n",
+ " <td>0.063</td>\n",
+ " <td>0.056</td>\n",
+ " <td>0.007</td>\n",
+ " <td>0.021</td>\n",
+ " <td>0.056</td>\n",
+ " <td>...</td>\n",
+ " <td>53.396</td>\n",
+ " <td>9.577</td>\n",
+ " <td>-0.556</td>\n",
+ " <td>-32.638</td>\n",
+ " <td>33.615</td>\n",
+ " <td>-18.208</td>\n",
+ " <td>-32.868</td>\n",
+ " <td>3.576</td>\n",
+ " <td>15.617</td>\n",
+ " <td>18.916</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>GCA_000474035.1_ASM47403v1.AGX32194.1</th>\n",
+ " <td>0.071</td>\n",
+ " <td>0.112</td>\n",
+ " <td>0.031</td>\n",
+ " <td>0.071</td>\n",
+ " <td>0.092</td>\n",
+ " <td>0.051</td>\n",
+ " <td>0.000</td>\n",
+ " <td>0.061</td>\n",
+ " <td>0.031</td>\n",
+ " <td>0.051</td>\n",
+ " <td>...</td>\n",
+ " <td>9.947</td>\n",
+ " <td>-0.399</td>\n",
+ " <td>6.355</td>\n",
+ " <td>-4.555</td>\n",
+ " <td>3.391</td>\n",
+ " <td>-3.494</td>\n",
+ " <td>-7.060</td>\n",
+ " <td>-0.879</td>\n",
+ " <td>4.413</td>\n",
+ " <td>-4.364</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>5 rows × 952 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " C0-1 C0-2 C0-3 C0-4 C0-5 \\\n",
+ "GCA_000474035.1_ASM47403v1.AGX32190.1 0.048 0.143 0.190 0.000 0.000 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32191.1 0.079 0.068 0.052 0.082 0.019 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32192.1 0.119 0.087 0.052 0.100 0.052 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32193.1 0.110 0.065 0.040 0.126 0.054 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32194.1 0.071 0.112 0.031 0.071 0.092 \n",
+ "\n",
+ " C0-6 C0-7 C0-8 C0-9 C0-10 ... \\\n",
+ "GCA_000474035.1_ASM47403v1.AGX32190.1 0.000 0.000 0.000 0.000 0.000 ... \n",
+ "GCA_000474035.1_ASM47403v1.AGX32191.1 0.060 0.025 0.014 0.030 0.088 ... \n",
+ "GCA_000474035.1_ASM47403v1.AGX32192.1 0.074 0.032 0.013 0.026 0.042 ... \n",
+ "GCA_000474035.1_ASM47403v1.AGX32193.1 0.063 0.056 0.007 0.021 0.056 ... \n",
+ "GCA_000474035.1_ASM47403v1.AGX32194.1 0.051 0.000 0.061 0.031 0.051 ... \n",
+ "\n",
+ " C8-91 C8-92 C8-93 C8-94 C8-95 \\\n",
+ "GCA_000474035.1_ASM47403v1.AGX32190.1 3.261 -0.408 -1.416 -2.440 1.106 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32191.1 46.974 5.258 -9.220 -38.018 33.755 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32192.1 32.604 5.340 2.275 -22.717 26.647 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32193.1 53.396 9.577 -0.556 -32.638 33.615 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32194.1 9.947 -0.399 6.355 -4.555 3.391 \n",
+ "\n",
+ " C8-96 C8-97 C8-98 C8-99 C8-100 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32190.1 -0.988 -2.093 0.879 0.848 0.490 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32191.1 -14.714 -28.453 9.052 15.603 18.250 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32192.1 -8.617 -15.550 0.318 7.316 18.674 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32193.1 -18.208 -32.868 3.576 15.617 18.916 \n",
+ "GCA_000474035.1_ASM47403v1.AGX32194.1 -3.494 -7.060 -0.879 4.413 -4.364 \n",
+ "\n",
+ "[5 rows x 952 columns]"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7c283d33",
+ "metadata": {},
+ "source": [
+ "### Validation data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 111,
+ "id": "80260859",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_val = pd.read_csv(\"partial_dataset_valid\\\\features.csv\", index_col=0)\n",
+ "y_val = pd.read_csv(\"partial_dataset_valid\\\\labels.csv\", index_col=0)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "58979bb5",
+ "metadata": {},
+ "source": [
+ ">### Distribution of features "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "34147bb3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<BarContainer object of 2 artists>"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "Labels = y[\"label\"].values.flatten()\n",
+ "fig, ax = plt.subplots()\n",
+ "ax.bar([\"pos\", \"neg\"], [Labels.sum(), (~Labels).sum()])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cd034bee",
+ "metadata": {},
+ "source": [
+ ">## Scaled & Unscaled data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a8331144",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "Power_Transformer = PowerTransformer()\n",
+ "X_train_s = Power_Transformer.fit_transform(X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9ce4aa02",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_=y.values.flatten()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "acc470f6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clf = linear_model.LogisticRegression(penalty=\"l2\", max_iter=1500,class_weight=\"balanced\",random_state=random_state)\n",
+ "clf_s = linear_model.LogisticRegression(penalty=\"l2\", max_iter=1500,class_weight=\"balanced\",random_state=random_state)\n",
+ "\n",
+ "clf.fit(X, y_).score(X, y_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "37e27546",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "clf_s.fit(X_train_s, y_).score(X_train_s, y_)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "34e4f9a9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "plt.scatter(clf.coef_, clf_s.coef_)\n",
+ "plt.xlabel(\"Unscaled Data\")\n",
+ "plt.ylabel(\"Scaled Data \")\n",
+ "plt.savefig(\"images/scatter.png\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cfb13c6",
+ "metadata": {},
+ "source": [
+ ">## Preprocessing "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "id": "06f03cfa",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 1.778, -2.526, 0.37 , ..., -1.117, -1.178, 2.501],\n",
+ " [ 38.792, -31.606, 24.462, ..., -20.403, -29.845, 32.04 ],\n",
+ " [ 32.207, -24.882, 6.19 , ..., -26.325, -22.241, 28.407],\n",
+ " ...,\n",
+ " [ 16.746, -12.385, 1.409, ..., -7.368, -8.797, 8.769],\n",
+ " [ 33.423, -29.203, 6.015, ..., -15.992, -16.769, 16.444],\n",
+ " [ 56.593, -42.186, 17.249, ..., -21.343, -35.433, 40.96 ]])"
+ ]
+ },
+ "execution_count": 72,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Select_k best\n",
+ "\n",
+ "X_select = SelectKBest(k=10).fit_transform(X, y)\n",
+ "X_select "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c45fa71",
+ "metadata": {},
+ "source": [
+ ">### Standard Scaler"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 116,
+ "id": "a0c9681d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(30000, 10)"
+ ]
+ },
+ "execution_count": 116,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Scaling\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "scaled_X = scaler.fit_transform(X_select)\n",
+ "scaled_X.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 117,
+ "id": "6255f780",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-22 {color: black;background-color: white;}#sk-container-id-22 pre{padding: 0;}#sk-container-id-22 div.sk-toggleable {background-color: white;}#sk-container-id-22 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-22 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-22 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-22 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-22 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-22 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-22 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-22 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-22 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-22 div.sk-item {position: relative;z-index: 1;}#sk-container-id-22 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-22 div.sk-item::before, #sk-container-id-22 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-22 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-22 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-22 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-22 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-22 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-22 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-22 div.sk-label-container {text-align: center;}#sk-container-id-22 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-22 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=10)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "PCA(n_components=10)"
+ ]
+ },
+ "execution_count": 117,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca_S = PCA(n_components=10)\n",
+ "pca_S.fit(scaled_X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 118,
+ "id": "e0e126a9",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.84205998, 0.07503553, 0.03734979, 0.02069677, 0.00808125,\n",
+ " 0.00608606, 0.00378793, 0.00295243, 0.0020629 , 0.00188736])"
+ ]
+ },
+ "execution_count": 118,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# project X on principal components\n",
+ "X_projected_S = pca_S.transform(scaled_X)\n",
+ "pca_S.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0d1f0591",
+ "metadata": {},
+ "source": [
+ ">### Robust Scaler"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "05bf1679",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[-0.92757123, 0.89773253, -0.85432862, ..., 0.93879245,\n",
+ " 0.97930195, -0.89558079],\n",
+ " [ 0.60021257, -0.48244521, 1.58580002, ..., -0.45324624,\n",
+ " -0.81059565, 0.7210015 ],\n",
+ " [ 0.32841119, -0.1633147 , -0.26485706, ..., -0.88068858,\n",
+ " -0.33582043, 0.52217814],\n",
+ " ...,\n",
+ " [-0.30975451, 0.42981051, -0.74909478, ..., 0.48760331,\n",
+ " 0.50359016, -0.55255165],\n",
+ " [ 0.37860261, -0.36839545, -0.28258172, ..., -0.13486593,\n",
+ " 0.00583791, -0.13252155],\n",
+ " [ 1.33496373, -0.98458691, 0.85524017, ..., -0.52109423,\n",
+ " -1.15949675, 1.20916678]])"
+ ]
+ },
+ "execution_count": 76,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Robust scaling\n",
+ "\n",
+ "robust_scaler = RobustScaler()\n",
+ "robust_scaled_X = robust_scaler.fit_transform(X_select)\n",
+ "robust_scaled_X"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "id": "f9267680",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=10)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "PCA(n_components=10)"
+ ]
+ },
+ "execution_count": 77,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca_RS = PCA(n_components=10)\n",
+ "pca_RS.fit(robust_scaled_X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "id": "75f34b4d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.83292906, 0.07883915, 0.04361342, 0.0208611 , 0.00782624,\n",
+ " 0.00581517, 0.00350253, 0.00276283, 0.00199479, 0.00185571])"
+ ]
+ },
+ "execution_count": 78,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# project X on principal components\n",
+ "X_projected_RS = pca_RS.transform(robust_scaled_X)\n",
+ "pca_RS.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d1898b02",
+ "metadata": {},
+ "source": [
+ ">### Power Transformer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "id": "42198ffd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Scaling\n",
+ "Power_Transformer = PowerTransformer()\n",
+ "PT_scaled_X = Power_Transformer.fit_transform(X_select)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "id": "ff96275d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=10)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "PCA(n_components=10)"
+ ]
+ },
+ "execution_count": 80,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca_PT = PCA(n_components=10)\n",
+ "pca_PT.fit(PT_scaled_X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "id": "ecb79a46",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.80883276, 0.08181252, 0.04849254, 0.02836388, 0.01067356,\n",
+ " 0.00670227, 0.0045779 , 0.00442211, 0.00333175, 0.0027907 ])"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_projected_PT = pca_PT.transform(PT_scaled_X)\n",
+ "pca_PT.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 121,
+ "id": "068945b1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df_s = pd.DataFrame(list(zip(y['label'],PT_scaled_X[:,0],PT_scaled_X[:,1])), columns =['label', 'X1','X2']) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 122,
+ "id": "217c49be",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<seaborn.axisgrid.JointGrid at 0x14c172f3220>"
+ ]
+ },
+ "execution_count": 122,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 600x600 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sns.jointplot(data=df_s, x='X1', y='X2', hue = 'label')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "id": "70fda7ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "df = pd.DataFrame(list(zip(y['label'],PT_scaled_X[:,0],PT_scaled_X[:,1])), columns =['label', 'X1','X2']) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "id": "a9bbd9da",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0, 0.5, 'X2')"
+ ]
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.scatter(df[\"X1\"],df[\"X2\"], c=df['label'], alpha = 0.5)\n",
+ " \n",
+ "# Adding Title to the Plot\n",
+ "plt.title(\"Scatter Plot\")\n",
+ " \n",
+ "# Setting the X and Y labels\n",
+ "plt.xlabel('X1')\n",
+ "plt.ylabel('X2')\n",
+ "\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 92,
+ "id": "60be3d78",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.lines.Line2D at 0x149a32dab80>"
+ ]
+ },
+ "execution_count": 92,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(X_projected_PT[:20])\n",
+ "plt.axhline(color='red')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dd7da253",
+ "metadata": {},
+ "source": [
+ ">### Correlation coefficient between original variables and the component"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 108,
+ "id": "54b1f9af",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\deprecation.py:101: FutureWarning: Attribute `n_features_` was deprecated in version 1.2 and will be removed in 1.4. Use `n_features_in_` instead.\n",
+ " warnings.warn(msg, category=FutureWarning)\n"
+ ]
+ }
+ ],
+ "source": [
+ "loadings = pca_PT.components_\n",
+ "num_pc = pca_PT.n_features_\n",
+ "pc_list = [\"PC\"+str(i) for i in list(range(1, num_pc+1))]\n",
+ "loadings_df = pd.DataFrame.from_dict(dict(zip(pc_list, loadings)))\n",
+ "#loadings_df['variable'] = X.columns.values\n",
+ "#loadings_df = loadings_df.set_index('variable')\n",
+ "loadings_df\n",
+ "pca_top = loadings_df.sort_values(['PC1', 'PC2'], ascending=False).nlargest(10, ['PC1', 'PC2']).abs()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 109,
+ "id": "e7a8b010",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
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+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>PC1</th>\n",
+ " <th>PC2</th>\n",
+ " <th>PC3</th>\n",
+ " <th>PC4</th>\n",
+ " <th>PC5</th>\n",
+ " <th>PC6</th>\n",
+ " <th>PC7</th>\n",
+ " <th>PC8</th>\n",
+ " <th>PC9</th>\n",
+ " <th>PC10</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>0.339731</td>\n",
+ " <td>0.088530</td>\n",
+ " <td>0.229708</td>\n",
+ " <td>0.060887</td>\n",
+ " <td>0.104661</td>\n",
+ " <td>0.291568</td>\n",
+ " <td>0.341021</td>\n",
+ " <td>0.298566</td>\n",
+ " <td>0.720148</td>\n",
+ " <td>0.016023</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>4</th>\n",
+ " <td>0.337926</td>\n",
+ " <td>0.116998</td>\n",
+ " <td>0.054731</td>\n",
+ " <td>0.219563</td>\n",
+ " <td>0.250094</td>\n",
+ " <td>0.695927</td>\n",
+ " <td>0.129301</td>\n",
+ " <td>0.399701</td>\n",
+ " <td>0.253943</td>\n",
+ " <td>0.181898</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>9</th>\n",
+ " <td>0.333616</td>\n",
+ " <td>0.159703</td>\n",
+ " <td>0.131577</td>\n",
+ " <td>0.190226</td>\n",
+ " <td>0.589104</td>\n",
+ " <td>0.492452</td>\n",
+ " <td>0.173971</td>\n",
+ " <td>0.050447</td>\n",
+ " <td>0.204886</td>\n",
+ " <td>0.381255</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>0.277394</td>\n",
+ " <td>0.000110</td>\n",
+ " <td>0.835644</td>\n",
+ " <td>0.339791</td>\n",
+ " <td>0.152215</td>\n",
+ " <td>0.096010</td>\n",
+ " <td>0.205807</td>\n",
+ " <td>0.081167</td>\n",
+ " <td>0.167109</td>\n",
+ " <td>0.006013</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>5</th>\n",
+ " <td>0.170659</td>\n",
+ " <td>0.955768</td>\n",
+ " <td>0.113885</td>\n",
+ " <td>0.186571</td>\n",
+ " <td>0.088794</td>\n",
+ " <td>0.008197</td>\n",
+ " <td>0.026853</td>\n",
+ " <td>0.027856</td>\n",
+ " <td>0.001478</td>\n",
+ " <td>0.012423</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>3</th>\n",
+ " <td>0.299740</td>\n",
+ " <td>0.128845</td>\n",
+ " <td>0.309269</td>\n",
+ " <td>0.852808</td>\n",
+ " <td>0.249716</td>\n",
+ " <td>0.046624</td>\n",
+ " <td>0.004091</td>\n",
+ " <td>0.074297</td>\n",
+ " <td>0.015382</td>\n",
+ " <td>0.017938</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>6</th>\n",
+ " <td>0.337868</td>\n",
+ " <td>0.034277</td>\n",
+ " <td>0.268483</td>\n",
+ " <td>0.036125</td>\n",
+ " <td>0.310902</td>\n",
+ " <td>0.183177</td>\n",
+ " <td>0.509508</td>\n",
+ " <td>0.504514</td>\n",
+ " <td>0.069840</td>\n",
+ " <td>0.402565</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>7</th>\n",
+ " <td>0.339714</td>\n",
+ " <td>0.089912</td>\n",
+ " <td>0.203472</td>\n",
+ " <td>0.005267</td>\n",
+ " <td>0.003376</td>\n",
+ " <td>0.374239</td>\n",
+ " <td>0.417130</td>\n",
+ " <td>0.691362</td>\n",
+ " <td>0.177138</td>\n",
+ " <td>0.107971</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>0.340875</td>\n",
+ " <td>0.009152</td>\n",
+ " <td>0.063839</td>\n",
+ " <td>0.084064</td>\n",
+ " <td>0.619970</td>\n",
+ " <td>0.013154</td>\n",
+ " <td>0.207362</td>\n",
+ " <td>0.056137</td>\n",
+ " <td>0.344858</td>\n",
+ " <td>0.568300</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>8</th>\n",
+ " <td>0.342662</td>\n",
+ " <td>0.116333</td>\n",
+ " <td>0.049489</td>\n",
+ " <td>0.161097</td>\n",
+ " <td>0.070777</td>\n",
+ " <td>0.054083</td>\n",
+ " <td>0.563055</td>\n",
+ " <td>0.000575</td>\n",
+ " <td>0.437693</td>\n",
+ " <td>0.569304</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PC1 PC2 PC3 PC4 PC5 PC6 PC7 \\\n",
+ "0 0.339731 0.088530 0.229708 0.060887 0.104661 0.291568 0.341021 \n",
+ "4 0.337926 0.116998 0.054731 0.219563 0.250094 0.695927 0.129301 \n",
+ "9 0.333616 0.159703 0.131577 0.190226 0.589104 0.492452 0.173971 \n",
+ "2 0.277394 0.000110 0.835644 0.339791 0.152215 0.096010 0.205807 \n",
+ "5 0.170659 0.955768 0.113885 0.186571 0.088794 0.008197 0.026853 \n",
+ "3 0.299740 0.128845 0.309269 0.852808 0.249716 0.046624 0.004091 \n",
+ "6 0.337868 0.034277 0.268483 0.036125 0.310902 0.183177 0.509508 \n",
+ "7 0.339714 0.089912 0.203472 0.005267 0.003376 0.374239 0.417130 \n",
+ "1 0.340875 0.009152 0.063839 0.084064 0.619970 0.013154 0.207362 \n",
+ "8 0.342662 0.116333 0.049489 0.161097 0.070777 0.054083 0.563055 \n",
+ "\n",
+ " PC8 PC9 PC10 \n",
+ "0 0.298566 0.720148 0.016023 \n",
+ "4 0.399701 0.253943 0.181898 \n",
+ "9 0.050447 0.204886 0.381255 \n",
+ "2 0.081167 0.167109 0.006013 \n",
+ "5 0.027856 0.001478 0.012423 \n",
+ "3 0.074297 0.015382 0.017938 \n",
+ "6 0.504514 0.069840 0.402565 \n",
+ "7 0.691362 0.177138 0.107971 \n",
+ "1 0.056137 0.344858 0.568300 \n",
+ "8 0.000575 0.437693 0.569304 "
+ ]
+ },
+ "execution_count": 109,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca_top"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 110,
+ "id": "b07eaabc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 1500x1000 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig = plt.figure(figsize=(15, 10))\n",
+ "ax = sns.heatmap(pca_top, annot=True, cmap='Spectral')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5892e5d2",
+ "metadata": {},
+ "source": [
+ ">## Model selection and validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 119,
+ "id": "7f79fbd7",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n",
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\feature_selection\\_univariate_selection.py:108: RuntimeWarning: invalid value encountered in divide\n",
+ " msb = ssbn / float(dfbn)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Select_k best\n",
+ "\n",
+ "X_select_val = SelectKBest(k=10).fit_transform(X_val, y_val)\n",
+ "\n",
+ "scaler = StandardScaler()\n",
+ "scaled_X_val = scaler.fit_transform(X_select_val)\n",
+ "\n",
+ "pca_S = PCA(n_components=10)\n",
+ "pca_S.fit(scaled_X_val)\n",
+ "\n",
+ "# project X on principal components\n",
+ "X_projected_val = pca_S.transform(scaled_X_val)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 120,
+ "id": "ea13f852",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " return self._fit(X, y)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "KNeighborsClassifier()\n",
+ "model score: 0.999\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "SVC(C=0.025, probability=True)\n",
+ "model score: 1.000\n",
+ "DecisionTreeClassifier()\n",
+ "model score: 0.981\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\pipeline.py:406: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
+ " self._final_estimator.fit(Xt, y, **fit_params_last_step)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "RandomForestClassifier()\n",
+ "model score: 1.000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "AdaBoostClassifier()\n",
+ "model score: 0.998\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\ensemble\\_gb.py:437: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GradientBoostingClassifier()\n",
+ "model score: 0.997\n",
+ "SGDClassifier()\n",
+ "model score: 1.000\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Users\\meryame.boudhar\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\utils\\validation.py:1141: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
+ " y = column_or_1d(y, warn=True)\n"
+ ]
+ }
+ ],
+ "source": [
+ "classifiers = [\n",
+ " KNeighborsClassifier(n_neighbors = 5),\n",
+ " SVC(kernel=\"rbf\", C=0.025, probability=True),\n",
+ " DecisionTreeClassifier(),\n",
+ " RandomForestClassifier(),\n",
+ " AdaBoostClassifier(),\n",
+ " GradientBoostingClassifier(),\n",
+ " SGDClassifier()\n",
+ " ]\n",
+ "\n",
+ "top_class = []\n",
+ "\n",
+ "for classifier in classifiers:\n",
+ " pipe = Pipeline(steps=[('classifier', classifier)])\n",
+ " \n",
+ " # training model\n",
+ " pipe.fit(X_projected_S, y) \n",
+ " print(classifier)\n",
+ " \n",
+ " acc_score = pipe.score(X_projected_val, y_val)\n",
+ " print(\"model score: %.3f\" % acc_score)\n",
+ " \n",
+ " # using the model to predict\n",
+ " y_pred = pipe.predict(X_projected_S)\n",
+ " \n",
+ "# target_names = [le_name_mapping[x] for x in le_name_mapping]\n",
+ "# print(classification_report(y_test, y_pred, target_names=target_names))\n",
+ " \n",
+ " if acc_score > 0.8:\n",
+ " top_class.append(classifier)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "2d5a4ce4",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/PCA.ipynb b/notebooks/PCA.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..09406c39b5456c6799d99c6f8a04d2354850e3d2
--- /dev/null
+++ b/notebooks/PCA.ipynb
@@ -0,0 +1,1068 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "78261375",
+ "metadata": {},
+ "source": [
+ "## PCA"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "115b83e8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import seaborn as sns\n",
+ "# from wpca import PCA\n",
+ "from matplotlib import pyplot as plt\n",
+ "from sklearn import decomposition\n",
+ "from sklearn import preprocessing\n",
+ "\n",
+ "\n",
+ "sns.set_style('darkgrid')\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7a5c46aa",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = pd.read_csv('../project_dataset/partial_dataset_train/features.csv', index_col=0)\n",
+ "X = X[:30000]\n",
+ "\n",
+ "y = pd.read_csv('../project_dataset/partial_dataset_train/labels.csv', index_col=0)\n",
+ "y = y[:30000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "de6edb1c",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# scaling the values\n",
+ "# std_scale = preprocessing.RobustScaler(unit_variance=True).fit(X)\n",
+ "std_scale = preprocessing.PowerTransformer().fit(X)\n",
+ "# std_scale = preprocessing.StandardScaler().fit(X)\n",
+ "X_scaled = std_scale.transform(X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "56306dff",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(30000, 952)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_scaled.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "fb447e1a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plt.plot(X_scaled[:5], c='black');"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "5f53ed2d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# def plot_results(PCA, X_scaled, weights=None, ncomp=2):\n",
+ "# # Compute the standard/weighted PCA\n",
+ "# if weights is None:\n",
+ "# kwds = {}\n",
+ "# else:\n",
+ "# kwds = {'weights': weights}\n",
+ " \n",
+ "# # Compute the PCA vectors & variance\n",
+ "# pca = PCA(n_components=10).fit(X_scaled, **kwds)\n",
+ " \n",
+ " \n",
+ "# # Create the plots\n",
+ "# fig, ax = plt.subplots(2, 2, figsize=(16, 6))\n",
+ " \n",
+ "# ax[0, 0].plot(X[:10].T, c='black', lw=1)\n",
+ "# #ax[1, 1].plot(Y[:10].T, c='black', lw=1)\n",
+ " \n",
+ "# ax[0, 1].plot(pca.components_[:ncomp].T, c='black')\n",
+ " \n",
+ "# ax[1, 0].plot(np.arange(1, 11), pca.explained_variance_ratio_)\n",
+ "# ax[1, 0].set_xlim(1, 10)\n",
+ "# ax[1, 0].set_ylim(0, None)\n",
+ " \n",
+ "# ax[0, 0].xaxis.set_major_formatter(plt.NullFormatter())\n",
+ "# ax[0, 1].xaxis.set_major_formatter(plt.NullFormatter())\n",
+ " \n",
+ "# ax[0, 0].set_title('Input Data')\n",
+ "# ax[0, 1].set_title('First {0} Principal Vectors'.format(ncomp))\n",
+ "# ax[1, 0].set_title('PCA variance ratio')\n",
+ "# ax[1, 0].set_xlabel('principal vector')\n",
+ "# ax[1, 0].set_ylabel('proportion of total variance')\n",
+ " \n",
+ "# fig.suptitle(PCA.__name__, fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c892ebdb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# plot_results(PCA, X_scaled);"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f6b4ec25",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>PCA(n_components=20)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=20)</pre></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "PCA(n_components=20)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "n_comps=20\n",
+ "pca = decomposition.PCA(n_components=n_comps)\n",
+ "pca.fit(X_scaled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "388b0efc",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[ 0.01475485, 0.01718488, -0.00511557, ..., 0.02383024,\n",
+ " 0.0662461 , 0.0833615 ],\n",
+ " [-0.07709265, -0.07167138, 0.0039343 , ..., 0.07228793,\n",
+ " -0.03770678, -0.00858669],\n",
+ " [-0.10176072, -0.04394365, 0.11839923, ..., 0.0819013 ,\n",
+ " -0.0759564 , -0.0413422 ],\n",
+ " ...,\n",
+ " [ 0.02284493, 0.03287975, -0.03115756, ..., 0.0302526 ,\n",
+ " 0.02710808, 0.03004009],\n",
+ " [ 0.02654399, -0.04213532, -0.07820169, ..., 0.02147233,\n",
+ " -0.02995369, -0.02317963],\n",
+ " [-0.01470402, 0.08331635, 0.05879395, ..., -0.0103606 ,\n",
+ " -0.00332572, 0.01605491]])"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca.components_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "ca241a5a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# project X on principal components\n",
+ "X_projected = pca.transform(X_scaled)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "2911b4d6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(30000, 20)"
+ ]
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X_projected.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "b5d86eac",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.legend.Legend at 0x2c399261400>"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(X_projected[:20, :5]);\n",
+ "plt.legend([f\"PC{i}\" for i in range(1, n_comps+1)])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "6ac9a515",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.lines.Line2D at 0x2c5698e08b0>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(X_projected[:100])\n",
+ "plt.legend([f\"PC{i}\" for i in range(1, n_comps+1)])\n",
+ "plt.axhline(color='red')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0e17d897",
+ "metadata": {},
+ "source": [
+ "### Correlation coefficient between original variables and the component"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e8d263e2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "loadings = pca.components_\n",
+ "num_pc = pca.n_features_\n",
+ "pc_list = [\"PC\"+str(i) for i in range(1, num_pc+1)]\n",
+ "loadings_df = pd.DataFrame.from_dict(dict(zip(pc_list, loadings)))\n",
+ "loadings_df['variable'] = X.columns.values\n",
+ "loadings_df = loadings_df.set_index('variable')\n",
+ "#loadings_df\n",
+ "pca_top = loadings_df.abs().nlargest(150, [f\"PC{i}\" for i in range(1, n_comps+1)])\n",
+ "#.sort_values([f\"PC{i}\" for i in range(1, n_comps+1)], ascending=False).iloc[:100, :]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "757a6e7e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>PC1</th>\n",
+ " <th>PC2</th>\n",
+ " <th>PC3</th>\n",
+ " <th>PC4</th>\n",
+ " <th>PC5</th>\n",
+ " <th>PC6</th>\n",
+ " <th>PC7</th>\n",
+ " <th>PC8</th>\n",
+ " <th>PC9</th>\n",
+ " <th>PC10</th>\n",
+ " <th>PC11</th>\n",
+ " <th>PC12</th>\n",
+ " <th>PC13</th>\n",
+ " <th>PC14</th>\n",
+ " <th>PC15</th>\n",
+ " <th>PC16</th>\n",
+ " <th>PC17</th>\n",
+ " <th>PC18</th>\n",
+ " <th>PC19</th>\n",
+ " <th>PC20</th>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>variable</th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>C6-1</th>\n",
+ " <td>0.094364</td>\n",
+ " <td>0.004809</td>\n",
+ " <td>0.011708</td>\n",
+ " <td>0.003995</td>\n",
+ " <td>0.007269</td>\n",
+ " <td>0.010671</td>\n",
+ " <td>0.001989</td>\n",
+ " <td>0.005817</td>\n",
+ " <td>0.001834</td>\n",
+ " <td>0.000830</td>\n",
+ " <td>0.004166</td>\n",
+ " <td>0.006603</td>\n",
+ " <td>0.004030</td>\n",
+ " <td>0.001637</td>\n",
+ " <td>0.012559</td>\n",
+ " <td>0.004071</td>\n",
+ " <td>0.000741</td>\n",
+ " <td>0.007088</td>\n",
+ " <td>0.000861</td>\n",
+ " <td>0.004428</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C8-2</th>\n",
+ " <td>0.094335</td>\n",
+ " <td>0.003938</td>\n",
+ " <td>0.003142</td>\n",
+ " <td>0.002032</td>\n",
+ " <td>0.001480</td>\n",
+ " <td>0.008251</td>\n",
+ " <td>0.001714</td>\n",
+ " <td>0.012962</td>\n",
+ " <td>0.000167</td>\n",
+ " <td>0.009476</td>\n",
+ " <td>0.000162</td>\n",
+ " <td>0.014762</td>\n",
+ " <td>0.006943</td>\n",
+ " <td>0.004110</td>\n",
+ " <td>0.015183</td>\n",
+ " <td>0.000028</td>\n",
+ " <td>0.007579</td>\n",
+ " <td>0.008801</td>\n",
+ " <td>0.000276</td>\n",
+ " <td>0.001722</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C8-7</th>\n",
+ " <td>0.094212</td>\n",
+ " <td>0.009569</td>\n",
+ " <td>0.007833</td>\n",
+ " <td>0.004827</td>\n",
+ " <td>0.008654</td>\n",
+ " <td>0.009908</td>\n",
+ " <td>0.000395</td>\n",
+ " <td>0.009083</td>\n",
+ " <td>0.005812</td>\n",
+ " <td>0.004656</td>\n",
+ " <td>0.007104</td>\n",
+ " <td>0.005153</td>\n",
+ " <td>0.006110</td>\n",
+ " <td>0.004854</td>\n",
+ " <td>0.014303</td>\n",
+ " <td>0.007331</td>\n",
+ " <td>0.000796</td>\n",
+ " <td>0.006502</td>\n",
+ " <td>0.000571</td>\n",
+ " <td>0.005174</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C8-37</th>\n",
+ " <td>0.094192</td>\n",
+ " <td>0.006703</td>\n",
+ " <td>0.008571</td>\n",
+ " <td>0.004005</td>\n",
+ " <td>0.005167</td>\n",
+ " <td>0.007882</td>\n",
+ " <td>0.000530</td>\n",
+ " <td>0.010709</td>\n",
+ " <td>0.001983</td>\n",
+ " <td>0.000202</td>\n",
+ " <td>0.009092</td>\n",
+ " <td>0.000131</td>\n",
+ " <td>0.002618</td>\n",
+ " <td>0.005126</td>\n",
+ " <td>0.021151</td>\n",
+ " <td>0.003411</td>\n",
+ " <td>0.002911</td>\n",
+ " <td>0.007367</td>\n",
+ " <td>0.003650</td>\n",
+ " <td>0.006918</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C8-78</th>\n",
+ " <td>0.093963</td>\n",
+ " <td>0.001661</td>\n",
+ " <td>0.000590</td>\n",
+ " <td>0.014251</td>\n",
+ " <td>0.019018</td>\n",
+ " <td>0.005164</td>\n",
+ " <td>0.006807</td>\n",
+ " <td>0.009376</td>\n",
+ " <td>0.000374</td>\n",
+ " <td>0.010970</td>\n",
+ " <td>0.003344</td>\n",
+ " <td>0.004034</td>\n",
+ " <td>0.007484</td>\n",
+ " <td>0.003574</td>\n",
+ " <td>0.020756</td>\n",
+ " <td>0.000035</td>\n",
+ " <td>0.002500</td>\n",
+ " <td>0.013397</td>\n",
+ " <td>0.000679</td>\n",
+ " <td>0.005198</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>...</th>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " <td>...</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C3-162</th>\n",
+ " <td>0.025281</td>\n",
+ " <td>0.007810</td>\n",
+ " <td>0.013260</td>\n",
+ " <td>0.006403</td>\n",
+ " <td>0.014822</td>\n",
+ " <td>0.025878</td>\n",
+ " <td>0.037792</td>\n",
+ " <td>0.050117</td>\n",
+ " <td>0.009329</td>\n",
+ " <td>0.015269</td>\n",
+ " <td>0.000550</td>\n",
+ " <td>0.011574</td>\n",
+ " <td>0.013063</td>\n",
+ " <td>0.007152</td>\n",
+ " <td>0.042635</td>\n",
+ " <td>0.008978</td>\n",
+ " <td>0.038412</td>\n",
+ " <td>0.058459</td>\n",
+ " <td>0.004927</td>\n",
+ " <td>0.042438</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C3-256</th>\n",
+ " <td>0.025280</td>\n",
+ " <td>0.001936</td>\n",
+ " <td>0.013932</td>\n",
+ " <td>0.012713</td>\n",
+ " <td>0.025485</td>\n",
+ " <td>0.011519</td>\n",
+ " <td>0.000124</td>\n",
+ " <td>0.014335</td>\n",
+ " <td>0.005401</td>\n",
+ " <td>0.021738</td>\n",
+ " <td>0.063571</td>\n",
+ " <td>0.040280</td>\n",
+ " <td>0.020679</td>\n",
+ " <td>0.002042</td>\n",
+ " <td>0.005583</td>\n",
+ " <td>0.067875</td>\n",
+ " <td>0.006473</td>\n",
+ " <td>0.000839</td>\n",
+ " <td>0.015825</td>\n",
+ " <td>0.006120</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C3-150</th>\n",
+ " <td>0.025267</td>\n",
+ " <td>0.000210</td>\n",
+ " <td>0.010079</td>\n",
+ " <td>0.019134</td>\n",
+ " <td>0.035275</td>\n",
+ " <td>0.010031</td>\n",
+ " <td>0.007218</td>\n",
+ " <td>0.062529</td>\n",
+ " <td>0.028280</td>\n",
+ " <td>0.028655</td>\n",
+ " <td>0.035965</td>\n",
+ " <td>0.038248</td>\n",
+ " <td>0.003056</td>\n",
+ " <td>0.012151</td>\n",
+ " <td>0.046070</td>\n",
+ " <td>0.029545</td>\n",
+ " <td>0.029873</td>\n",
+ " <td>0.040879</td>\n",
+ " <td>0.016229</td>\n",
+ " <td>0.015040</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C3-193</th>\n",
+ " <td>0.025252</td>\n",
+ " <td>0.011464</td>\n",
+ " <td>0.020832</td>\n",
+ " <td>0.017155</td>\n",
+ " <td>0.019764</td>\n",
+ " <td>0.005423</td>\n",
+ " <td>0.008035</td>\n",
+ " <td>0.039238</td>\n",
+ " <td>0.005488</td>\n",
+ " <td>0.013552</td>\n",
+ " <td>0.038498</td>\n",
+ " <td>0.066579</td>\n",
+ " <td>0.000088</td>\n",
+ " <td>0.020262</td>\n",
+ " <td>0.022900</td>\n",
+ " <td>0.076927</td>\n",
+ " <td>0.004304</td>\n",
+ " <td>0.004802</td>\n",
+ " <td>0.010379</td>\n",
+ " <td>0.011499</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C3-98</th>\n",
+ " <td>0.025207</td>\n",
+ " <td>0.015316</td>\n",
+ " <td>0.003634</td>\n",
+ " <td>0.010928</td>\n",
+ " <td>0.009939</td>\n",
+ " <td>0.005999</td>\n",
+ " <td>0.012088</td>\n",
+ " <td>0.016016</td>\n",
+ " <td>0.014927</td>\n",
+ " <td>0.000234</td>\n",
+ " <td>0.044490</td>\n",
+ " <td>0.010219</td>\n",
+ " <td>0.028347</td>\n",
+ " <td>0.055400</td>\n",
+ " <td>0.033190</td>\n",
+ " <td>0.061125</td>\n",
+ " <td>0.027284</td>\n",
+ " <td>0.011990</td>\n",
+ " <td>0.013078</td>\n",
+ " <td>0.005553</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>150 rows × 20 columns</p>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " PC1 PC2 PC3 PC4 PC5 PC6 \\\n",
+ "variable \n",
+ "C6-1 0.094364 0.004809 0.011708 0.003995 0.007269 0.010671 \n",
+ "C8-2 0.094335 0.003938 0.003142 0.002032 0.001480 0.008251 \n",
+ "C8-7 0.094212 0.009569 0.007833 0.004827 0.008654 0.009908 \n",
+ "C8-37 0.094192 0.006703 0.008571 0.004005 0.005167 0.007882 \n",
+ "C8-78 0.093963 0.001661 0.000590 0.014251 0.019018 0.005164 \n",
+ "... ... ... ... ... ... ... \n",
+ "C3-162 0.025281 0.007810 0.013260 0.006403 0.014822 0.025878 \n",
+ "C3-256 0.025280 0.001936 0.013932 0.012713 0.025485 0.011519 \n",
+ "C3-150 0.025267 0.000210 0.010079 0.019134 0.035275 0.010031 \n",
+ "C3-193 0.025252 0.011464 0.020832 0.017155 0.019764 0.005423 \n",
+ "C3-98 0.025207 0.015316 0.003634 0.010928 0.009939 0.005999 \n",
+ "\n",
+ " PC7 PC8 PC9 PC10 PC11 PC12 \\\n",
+ "variable \n",
+ "C6-1 0.001989 0.005817 0.001834 0.000830 0.004166 0.006603 \n",
+ "C8-2 0.001714 0.012962 0.000167 0.009476 0.000162 0.014762 \n",
+ "C8-7 0.000395 0.009083 0.005812 0.004656 0.007104 0.005153 \n",
+ "C8-37 0.000530 0.010709 0.001983 0.000202 0.009092 0.000131 \n",
+ "C8-78 0.006807 0.009376 0.000374 0.010970 0.003344 0.004034 \n",
+ "... ... ... ... ... ... ... \n",
+ "C3-162 0.037792 0.050117 0.009329 0.015269 0.000550 0.011574 \n",
+ "C3-256 0.000124 0.014335 0.005401 0.021738 0.063571 0.040280 \n",
+ "C3-150 0.007218 0.062529 0.028280 0.028655 0.035965 0.038248 \n",
+ "C3-193 0.008035 0.039238 0.005488 0.013552 0.038498 0.066579 \n",
+ "C3-98 0.012088 0.016016 0.014927 0.000234 0.044490 0.010219 \n",
+ "\n",
+ " PC13 PC14 PC15 PC16 PC17 PC18 \\\n",
+ "variable \n",
+ "C6-1 0.004030 0.001637 0.012559 0.004071 0.000741 0.007088 \n",
+ "C8-2 0.006943 0.004110 0.015183 0.000028 0.007579 0.008801 \n",
+ "C8-7 0.006110 0.004854 0.014303 0.007331 0.000796 0.006502 \n",
+ "C8-37 0.002618 0.005126 0.021151 0.003411 0.002911 0.007367 \n",
+ "C8-78 0.007484 0.003574 0.020756 0.000035 0.002500 0.013397 \n",
+ "... ... ... ... ... ... ... \n",
+ "C3-162 0.013063 0.007152 0.042635 0.008978 0.038412 0.058459 \n",
+ "C3-256 0.020679 0.002042 0.005583 0.067875 0.006473 0.000839 \n",
+ "C3-150 0.003056 0.012151 0.046070 0.029545 0.029873 0.040879 \n",
+ "C3-193 0.000088 0.020262 0.022900 0.076927 0.004304 0.004802 \n",
+ "C3-98 0.028347 0.055400 0.033190 0.061125 0.027284 0.011990 \n",
+ "\n",
+ " PC19 PC20 \n",
+ "variable \n",
+ "C6-1 0.000861 0.004428 \n",
+ "C8-2 0.000276 0.001722 \n",
+ "C8-7 0.000571 0.005174 \n",
+ "C8-37 0.003650 0.006918 \n",
+ "C8-78 0.000679 0.005198 \n",
+ "... ... ... \n",
+ "C3-162 0.004927 0.042438 \n",
+ "C3-256 0.015825 0.006120 \n",
+ "C3-150 0.016229 0.015040 \n",
+ "C3-193 0.010379 0.011499 \n",
+ "C3-98 0.013078 0.005553 \n",
+ "\n",
+ "[150 rows x 20 columns]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca_top"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "d64da355",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1300x700 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(13, 7))\n",
+ "plt.subplot(1, 2, 1)\n",
+ "pcs = pca.components_\n",
+ "\n",
+ "plt.xlabel(f\"PC1 ({round(pca.explained_variance_ratio_[0]*100, 2)}%)\", fontsize=12)\n",
+ "plt.ylabel(f\"PC2 ({round(pca.explained_variance_ratio_[1]*100, 2)}%)\", fontsize=12)\n",
+ "plt.xlim([-0.15, 0.15])\n",
+ "plt.ylim([-0.15, 0.15])\n",
+ "\n",
+ "for i, (x, y) in enumerate(zip(pcs[0, :], pcs[1, :])):\n",
+ " # plot line between origin and point (x, y)\n",
+ " plt.scatter([0, x], [0, y], s=1.5, alpha=0.5)\n",
+ "\n",
+ "plt.subplot(1, 2, 2)\n",
+ "ax = sns.heatmap(loadings_df.abs(), annot=False, cmap='Spectral')\n",
+ "plt.yticks([])\n",
+ "plt.ylabel(\"Features\", fontsize=15)\n",
+ "plt.savefig(\"../images/pca.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 103,
+ "id": "d54db2b3-b079-41bc-8755-dc0f522ef14e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pcs = pca.components_\n",
+ "\n",
+ "plt.figure(figsize=(5, 5))\n",
+ "plt.xlabel(f\"PC1 ({round(pca.explained_variance_ratio_[0]*100, 2)}%)\")\n",
+ "plt.ylabel(f\"PC2 ({round(pca.explained_variance_ratio_[1]*100, 2)}%)\")\n",
+ "plt.xlim([-0.15, 0.15])\n",
+ "plt.ylim([-0.15, 0.15])\n",
+ "\n",
+ "for i, (x, y) in enumerate(zip(pcs[0, :], pcs[1, :])):\n",
+ " # plot line between origin and point (x, y)\n",
+ " plt.scatter([0, x], [0, y], s=0.8, alpha=0.5)\n",
+ "\n",
+ "# plt.savefig(\"../images/biplot_pca.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "id": "8f4689aa-66fb-4cdb-9f04-4764609398f0",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 500x500 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pcs = pca.components_\n",
+ "\n",
+ "plt.figure(figsize=(5, 5))\n",
+ "plt.xlabel(f\"PC1 ({round(pca.explained_variance_ratio_[0]*100, 2)}%)\")\n",
+ "plt.ylabel(f\"PC2 ({round(pca.explained_variance_ratio_[1]*100, 2)}%)\")\n",
+ "plt.xlim([-0.15, 0.15])\n",
+ "plt.ylim([-0.15, 0.15])\n",
+ "\n",
+ "for i, (x, y) in enumerate(zip(pcs[0, :], pcs[1, :])):\n",
+ " # plot line between origin and point (x, y)\n",
+ " plt.scatter([0, x], [0, y], s=0.8)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "id": "9fd35ca1",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'cluster' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[58], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mcluster\u001b[49m\u001b[38;5;241m.\u001b[39mpcaplot(x\u001b[38;5;241m=\u001b[39mloadings[\u001b[38;5;241m0\u001b[39m], y\u001b[38;5;241m=\u001b[39mloadings[\u001b[38;5;241m1\u001b[39m], labels\u001b[38;5;241m=\u001b[39mX\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mvalues, \n\u001b[0;32m 2\u001b[0m var1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m(pca\u001b[38;5;241m.\u001b[39mexplained_variance_ratio_[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m2\u001b[39m),\n\u001b[0;32m 3\u001b[0m var2\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m(pca\u001b[38;5;241m.\u001b[39mexplained_variance_ratio_[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m2\u001b[39m))\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'cluster' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "cluster.pcaplot(x=loadings[0], y=loadings[1], labels=X.columns.values, \n",
+ " var1=round(pca.explained_variance_ratio_[0]*100, 2),\n",
+ " var2=round(pca.explained_variance_ratio_[1]*100, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "id": "b75249ad",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "NameError",
+ "evalue": "name 'PCA' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[59], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m pca_scores \u001b[38;5;241m=\u001b[39m \u001b[43mPCA\u001b[49m()\u001b[38;5;241m.\u001b[39mfit_transform(X_scaled)\n\u001b[0;32m 2\u001b[0m cluster\u001b[38;5;241m.\u001b[39mbiplot(cscore\u001b[38;5;241m=\u001b[39mpca_scores, loadings\u001b[38;5;241m=\u001b[39mloadings, labels\u001b[38;5;241m=\u001b[39mX\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mvalues, var1\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m(pca\u001b[38;5;241m.\u001b[39mexplained_variance_ratio_[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m2\u001b[39m),\n\u001b[0;32m 3\u001b[0m var2\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mround\u001b[39m(pca\u001b[38;5;241m.\u001b[39mexplained_variance_ratio_[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m100\u001b[39m, \u001b[38;5;241m2\u001b[39m))\n",
+ "\u001b[1;31mNameError\u001b[0m: name 'PCA' is not defined"
+ ]
+ }
+ ],
+ "source": [
+ "pca_scores = PCA().fit_transform(X_scaled)\n",
+ "cluster.biplot(cscore=pca_scores, loadings=loadings, labels=X.columns.values, var1=round(pca.explained_variance_ratio_[0]*100, 2),\n",
+ " var2=round(pca.explained_variance_ratio_[1]*100, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "id": "f443d7a9",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'plotly'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[60], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mplotly\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexpress\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpx\u001b[39;00m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecomposition\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m PCA\n\u001b[0;32m 4\u001b[0m df \u001b[38;5;241m=\u001b[39m px\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miris()\n",
+ "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
+ ]
+ }
+ ],
+ "source": [
+ "import plotly.express as px\n",
+ "from sklearn.decomposition import PCA\n",
+ "\n",
+ "df = px.data.iris()\n",
+ "features = [\"sepal_width\", \"sepal_length\", \"petal_width\", \"petal_length\"]\n",
+ "\n",
+ "pca = PCA()\n",
+ "components = pca.fit_transform(df[features])\n",
+ "labels = {\n",
+ " str(i): f\"PC {i+1} ({var:.1f}%)\"\n",
+ " for i, var in enumerate(pca.explained_variance_ratio_ * 100)\n",
+ "}\n",
+ "\n",
+ "fig = px.scatter_matrix(\n",
+ " components,\n",
+ " labels=labels,\n",
+ " dimensions=range(4),\n",
+ " color=df[\"species\"]\n",
+ ")\n",
+ "fig.update_traces(diagonal_visible=False)\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e8b3f84a",
+ "metadata": {},
+ "source": [
+ "### Determine the Explained Variance"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "129f0967",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.11633078, 0.04561456, 0.03052545, 0.01788935, 0.01452156,\n",
+ " 0.01353796, 0.0107441 , 0.00792006, 0.00669089, 0.0056969 ,\n",
+ " 0.00492581, 0.00449132, 0.00429554, 0.00398293, 0.00380152,\n",
+ " 0.00361098, 0.00333201, 0.00316772, 0.00314459, 0.00310312])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "id": "1b718a33-2c17-421a-8a0b-ce080ec27b3f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([0.09023084, 0.04435458, 0.02898306, 0.01704717, 0.01481063,\n",
+ " 0.01368144, 0.01225845, 0.01048634, 0.00833601, 0.00716223,\n",
+ " 0.00617097, 0.00504525, 0.00478248, 0.00456127, 0.00431163,\n",
+ " 0.00391847, 0.00377737, 0.00371009, 0.00366188, 0.00357122])"
+ ]
+ },
+ "execution_count": 68,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca.explained_variance_ratio_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "id": "c69ca234",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(5,)"
+ ]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pca.explained_variance_ratio_.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "0605c283",
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (15,) and arg 1 with shape (5,).",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[1;32mIn[63], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbar\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marange\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m15\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpca\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplained_variance_ratio_\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlim([\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m11\u001b[39m])\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNumber of PCs\u001b[39m\u001b[38;5;124m\"\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16\u001b[39m)\n",
+ "File \u001b[1;32m~\\.conda\\envs\\ml\\lib\\site-packages\\matplotlib\\pyplot.py:2367\u001b[0m, in \u001b[0;36mbar\u001b[1;34m(x, height, width, bottom, align, data, **kwargs)\u001b[0m\n\u001b[0;32m 2363\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mbar)\n\u001b[0;32m 2364\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbar\u001b[39m(\n\u001b[0;32m 2365\u001b[0m x, height, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.8\u001b[39m, bottom\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m 2366\u001b[0m data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m-> 2367\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbar\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2368\u001b[0m \u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbottom\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbottom\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malign\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malign\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 2369\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+ "File \u001b[1;32m~\\.conda\\envs\\ml\\lib\\site-packages\\matplotlib\\__init__.py:1423\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[1;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1420\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[0;32m 1421\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m 1422\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1423\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1425\u001b[0m bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1426\u001b[0m auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[0;32m 1427\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+ "File \u001b[1;32m~\\.conda\\envs\\ml\\lib\\site-packages\\matplotlib\\axes\\_axes.py:2391\u001b[0m, in \u001b[0;36mAxes.bar\u001b[1;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[0;32m 2388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m yerr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 2389\u001b[0m yerr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_convert_dx(yerr, y0, y, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconvert_yunits)\n\u001b[1;32m-> 2391\u001b[0m x, height, width, y, linewidth, hatch \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast_arrays\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 2392\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Make args iterable too.\u001b[39;49;00m\n\u001b[0;32m 2393\u001b[0m \u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43matleast_1d\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlinewidth\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhatch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2395\u001b[0m \u001b[38;5;66;03m# Now that units have been converted, set the tick locations.\u001b[39;00m\n\u001b[0;32m 2396\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m orientation \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvertical\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
+ "File \u001b[1;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
+ "File \u001b[1;32m~\\.conda\\envs\\ml\\lib\\site-packages\\numpy\\lib\\stride_tricks.py:540\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[1;34m(subok, *args)\u001b[0m\n\u001b[0;32m 533\u001b[0m \u001b[38;5;66;03m# nditer is not used here to avoid the limit of 32 arrays.\u001b[39;00m\n\u001b[0;32m 534\u001b[0m \u001b[38;5;66;03m# Otherwise, something like the following one-liner would suffice:\u001b[39;00m\n\u001b[0;32m 535\u001b[0m \u001b[38;5;66;03m# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],\u001b[39;00m\n\u001b[0;32m 536\u001b[0m \u001b[38;5;66;03m# order='C').itviews\u001b[39;00m\n\u001b[0;32m 538\u001b[0m args \u001b[38;5;241m=\u001b[39m [np\u001b[38;5;241m.\u001b[39marray(_m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, subok\u001b[38;5;241m=\u001b[39msubok) \u001b[38;5;28;01mfor\u001b[39;00m _m \u001b[38;5;129;01min\u001b[39;00m args]\n\u001b[1;32m--> 540\u001b[0m shape \u001b[38;5;241m=\u001b[39m \u001b[43m_broadcast_shape\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 542\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mall\u001b[39m(array\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m==\u001b[39m shape \u001b[38;5;28;01mfor\u001b[39;00m array \u001b[38;5;129;01min\u001b[39;00m args):\n\u001b[0;32m 543\u001b[0m \u001b[38;5;66;03m# Common case where nothing needs to be broadcasted.\u001b[39;00m\n\u001b[0;32m 544\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m args\n",
+ "File \u001b[1;32m~\\.conda\\envs\\ml\\lib\\site-packages\\numpy\\lib\\stride_tricks.py:422\u001b[0m, in \u001b[0;36m_broadcast_shape\u001b[1;34m(*args)\u001b[0m\n\u001b[0;32m 417\u001b[0m \u001b[38;5;124;03m\"\"\"Returns the shape of the arrays that would result from broadcasting the\u001b[39;00m\n\u001b[0;32m 418\u001b[0m \u001b[38;5;124;03msupplied arrays against each other.\u001b[39;00m\n\u001b[0;32m 419\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 420\u001b[0m \u001b[38;5;66;03m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[39;00m\n\u001b[0;32m 421\u001b[0m \u001b[38;5;66;03m# consistently\u001b[39;00m\n\u001b[1;32m--> 422\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbroadcast\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m32\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 423\u001b[0m \u001b[38;5;66;03m# unfortunately, it cannot handle 32 or more arguments directly\u001b[39;00m\n\u001b[0;32m 424\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m pos \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m32\u001b[39m, \u001b[38;5;28mlen\u001b[39m(args), \u001b[38;5;241m31\u001b[39m):\n\u001b[0;32m 425\u001b[0m \u001b[38;5;66;03m# ironically, np.broadcast does not properly handle np.broadcast\u001b[39;00m\n\u001b[0;32m 426\u001b[0m \u001b[38;5;66;03m# objects (it treats them as scalars)\u001b[39;00m\n\u001b[0;32m 427\u001b[0m \u001b[38;5;66;03m# use broadcasting to avoid allocating the full array\u001b[39;00m\n",
+ "\u001b[1;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape. Mismatch is between arg 0 with shape (15,) and arg 1 with shape (5,)."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.bar(np.arange(15), pca.explained_variance_ratio_)\n",
+ "plt.xlim([-1, 11])\n",
+ "plt.xlabel(\"Number of PCs\", fontsize=16)\n",
+ "plt.ylabel(\"Fraction of variance explained\", fontsize=16)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6146fc28",
+ "metadata": {},
+ "source": [
+ "### Visualize the Principal Component"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/final-evaluation.ipynb b/notebooks/final-evaluation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a8828c7d4e45d77868253061081020a2cf7a174c
--- /dev/null
+++ b/notebooks/final-evaluation.ipynb
@@ -0,0 +1,150 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "70e19e41-142a-4995-b4d8-726bb95c9e2f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, PowerTransformer, LabelEncoder\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.feature_selection import SelectKBest, SelectPercentile\n",
+ "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, AdaBoostClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+ "from sklearn.model_selection import StratifiedGroupKFold, GridSearchCV\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "595ab7db-af7b-4d0b-9a6d-479eaef2684b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: total: 1min 37s\n",
+ "Wall time: 1min 38s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "outfile = \"test\"\n",
+ "DATA_DIR = f\"../project_dataset/partial_dataset_{outfile}\"\n",
+ "\n",
+ "X = pd.read_csv(f\"{DATA_DIR}/features.csv\", index_col=0)\n",
+ "X = X.values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "aff93c5f-37d6-4cc2-9fa0-53c27da346c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "model = pickle.load(open(f\"../models/OneSidedSelection_best_model_250000_LogisticRegression_0.7_0.76.pkl\", 'rb'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "e46f24c8-73ea-4015-8eb3-b48f66380767",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=20)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.18, class_weight='balanced', max_iter=1500,\n",
+ " solver='saga')</pre></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "7e7602fe-9977-4f24-8750-21107e1301a7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_pred = model.predict(X)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "6d9c5acc-a2bb-4ef6-94f1-6793924423c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "y_pred = y_pred.astype(np.uint8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "b03bdb80-d3f4-46cd-8179-c721c4e37e7d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "np.save(f\"../outputs/{outfile}.npy\", y_pred)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/ml-pipelines.ipynb b/notebooks/ml-pipelines.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..a2fb0338270f318149338bdb905d5c6e554f8ec9
--- /dev/null
+++ b/notebooks/ml-pipelines.ipynb
@@ -0,0 +1,609 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "da350838-0cdd-43c0-b7bb-3e9d5f07a789",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'1.1.3'"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import sklearn\n",
+ "sklearn.__version__"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d4a009df",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "6f348319",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, PowerTransformer, LabelEncoder\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.feature_selection import SelectKBest, SelectPercentile\n",
+ "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, AdaBoostClassifier\n",
+ "from sklearn.tree import DecisionTreeClassifier\n",
+ "from sklearn.svm import SVC\n",
+ "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
+ "from sklearn.model_selection import StratifiedGroupKFold, GridSearchCV\n",
+ "from sklearn.model_selection import train_test_split"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "4445ebe2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: total: 1min 35s\n",
+ "Wall time: 1min 35s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "DATA_DIR = \"../project_dataset\"\n",
+ "TRAIN_DIR = f\"{DATA_DIR}/partial_dataset_train\"\n",
+ "\n",
+ "# first_n = -1\n",
+ "# first_n = 30_000\n",
+ "first_n = 250_000\n",
+ "\n",
+ "X_ = pd.read_csv(f\"{TRAIN_DIR}/features.csv\", index_col=0)\n",
+ "y_ = pd.read_csv(f\"{TRAIN_DIR}/labels.csv\", index_col=0)\n",
+ "cl_df_ = pd.read_csv(f\"{TRAIN_DIR}/complete_labels.csv\", index_col=0)\n",
+ "\n",
+ "if first_n > 0:\n",
+ " X = X_[:first_n]\n",
+ " y = y_[:first_n]\n",
+ " cl_df = cl_df_[:first_n]\n",
+ "else:\n",
+ " X = X_\n",
+ " y = y_\n",
+ " cl_df = cl_df_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "c6b074a1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if \"group_id\" in cl_df:\n",
+ " cl_df.drop(\"group_id\", axis=1)\n",
+ "\n",
+ "cl_df[\"group_id\"] = cl_df.astype(bool).groupby(cl_df.columns.tolist(), sort=False).ngroup() + 1\n",
+ "min_ = cl_df[\"group_id\"].min()\n",
+ "max_ = cl_df[\"group_id\"].max()\n",
+ "\n",
+ "def f(r):\n",
+ " if r[\"label\"] == False:\n",
+ " r[\"group_id\"] = np.random.randint(min_, max_, size=1)[0]\n",
+ " return r[\"group_id\"]\n",
+ "\n",
+ "group_ids = cl_df[[\"label\", \"group_id\"]].apply(f, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b0edc83b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = X.values#.astype(np.float32)\n",
+ "y = LabelEncoder().fit_transform(y.values.squeeze())#.astype(np.uint8)\n",
+ "groups = group_ids.to_numpy()#.astype(np.uint16)\n",
+ "\n",
+ "del X_, y_, cl_df, cl_df_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d69beef-13c5-44bb-8527-3573f3717375",
+ "metadata": {},
+ "source": [
+ "## Undersampling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "bc3f406a-0b9c-43aa-b101-763ae6929567",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from imblearn.under_sampling import *\n",
+ "from collections import Counter"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "c089fc66-b52b-461d-a067-c62820563d54",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# undersample = EditedNearestNeighbours(sampling_strategy=\"majority\",\n",
+ "# n_neighbors=11,\n",
+ "# kind_sel=\"all\",\n",
+ "# n_jobs=-1) # 7 < k < 11\n",
+ "\n",
+ "undersample = OneSidedSelection(sampling_strategy=\"majority\", n_neighbors=3, n_seeds_S=100, n_jobs=-1) # 3 < k < 11"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "271da0ac-b017-4ac4-a75b-8d4c31c2c511",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original dataset shape Counter({0: 246238, 1: 3762})\n",
+ "Resampled dataset shape Counter({0: 239656, 1: 3762})\n",
+ "CPU times: total: 2h 22min 1s\n",
+ "Wall time: 8min 2s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "# undersample = RepeatedEditedNearestNeighbours(sampling_strategy=\"majority\", n_jobs=-1) #not good enough\n",
+ "# undersample = NeighbourhoodCleaningRule(sampling_strategy=\"majority\", n_jobs=-1, threshold_cleaning=0.2)\n",
+ "# undersample = TomekLinks(sampling_strategy=\"majority\", n_jobs=-1) # not much reduction\n",
+ "\n",
+ "# undersample = NearMiss(sampling_strategy=1/35, n_jobs=-1) # ratio of 1/25 to 1/50 works well on valid set\n",
+ "X_res, y_res = undersample.fit_resample(X, y)\n",
+ "print('Original dataset shape %s' % Counter(y))\n",
+ "print('Resampled dataset shape %s' % Counter(y_res))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "68833452-9559-40e0-8971-e3a89fbefde1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = X_res\n",
+ "y = y_res\n",
+ "groups = groups[undersample.sample_indices_]\n",
+ "\n",
+ "del X_res, y_res"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "6ff74e5d",
+ "metadata": {},
+ "source": [
+ "## Getting groups for each protein in the dataset for KFold"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b0c4322a",
+ "metadata": {},
+ "source": [
+ "## Define K fold"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ecb4ecd6",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# n_splits = 5\n",
+ "# cv = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7705fc6b",
+ "metadata": {},
+ "source": [
+ "## Building pipes for each ML algorithm\n",
+ "We'll be using grid search. The following code was just a trial on how to use pipes in sklearn."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a455a859",
+ "metadata": {},
+ "source": [
+ "```python\n",
+ "n_components = 10\n",
+ "\n",
+ "pipe_lr = Pipeline(steps=[\n",
+ " (\"lr_scaler\", StandardScaler()),\n",
+ " (\"lr_dim_reduce\", PCA(n_components=n_components)),\n",
+ " (\"lr_clf\", LinearRegression(n_jobs=-1))]\n",
+ ")\n",
+ "\n",
+ "pipe_rf = Pipeline(steps=[\n",
+ " (\"rf_scaler\", StandardScaler()),\n",
+ " (\"rf_dim_reduce\", PCA(n_components=n_components)),\n",
+ " (\"rf_clf\", RandomForestClassifier(n_jobs=-1))]\n",
+ ")\n",
+ "\n",
+ "pipe_svm = Pipeline(steps=[\n",
+ " (\"svm_scaler\", StandardScaler()),\n",
+ " (\"svm_dim_reduce\", PCA(n_components=n_components)),\n",
+ " (\"svm_clf\", SVC())]\n",
+ ")\n",
+ "\n",
+ "pipelines = {\n",
+ " \"Linear Regression\": pipe_lr,\n",
+ " \"Random Forest\": pipe_rf,\n",
+ " \"Support Vector Machine\": pipe_svm, \n",
+ "}\n",
+ "\n",
+ "scores = {key: [] for key in pipelines.keys()}\n",
+ "\n",
+ "for train_id, test_id in cv.split(X, y, groups):\n",
+ "\n",
+ " X_train = X[train_id]\n",
+ " y_train = y[train_id]\n",
+ " X_test = X[test_id]\n",
+ " y_test = y[test_id]\n",
+ "\n",
+ " for clf_name, pipe in pipelines.items():\n",
+ "\n",
+ " pipe.fit(X_train, y_train)\n",
+ " s = pipe.score(X_test, y_test)\n",
+ " scores[clf_name].append(round(s, 3))\n",
+ "\n",
+ " print(\"#\", end=\"\")\n",
+ "print(\"\\n\")\n",
+ "\n",
+ "pd.DataFrame(scores)\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d898703c",
+ "metadata": {},
+ "source": [
+ "## Combining Cross validation and all the pipes in GridSearchCV\n",
+ "This takes time. ALOT OF TIME! So, only choose the right algorithms and parameters for our problem"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e07adab8-e89c-4959-9fa1-7b540b403a70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_metrics(y_true, y_pred):\n",
+ " from sklearn.metrics import balanced_accuracy_score, precision_score, roc_auc_score, f1_score\n",
+ " return {\n",
+ " \"b_acc\": round(balanced_accuracy_score(y_true, y_pred), 2),\n",
+ " \"prec\": round(precision_score(y_true, y_pred), 2),\n",
+ " \"f1\": round(f1_score(y_true, y_pred), 2),\n",
+ " \"roc\": round(roc_auc_score(y_true, y_pred), 2)\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "91386743",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ " # # Logistic Regression\n",
+ " # {\n",
+ " # \"dim_reduce__n_components\": np.arange(5, 16, 5),\n",
+ " # \"clf\": [LogisticRegression()],\n",
+ " # \"clf__penalty\": [\"l2\"],\n",
+ " # \"clf__C\": np.logspace(0, 4, 5),\n",
+ " # \"clf__solver\": [\"newton-cg\", \"saga\", \"sag\", \"liblinear\"]\n",
+ " # },\n",
+ " # # Random Forests\n",
+ " # {\n",
+ " # \"scaler\": [RobustScaler()],\n",
+ " # \"scaler__unit_variance\": [True, False],\n",
+ " # \"dim_reduce\": [SelectPercentile()],\n",
+ " # \"dim_reduce__percentile\": np.arange(10, 51, 10),\n",
+ " # \"clf\": [RandomForestClassifier()],\n",
+ " # \"clf__n_estimators\": np.arange(100, 201, 50),\n",
+ " # \"clf__criterion\": [\"gini\", \"entropy\", \"log_loss\"],\n",
+ " # \"clf__max_features\": [\"sqrt\", \"log2\"],\n",
+ " # \"clf__class_weight\": [\"balanced\", \"balanced_subsample\"]\n",
+ " # },\n",
+ " # # Support Vector Machine\n",
+ " # {\n",
+ " # \"pca__n_components\": [2, 20],\n",
+ " # \"clf\": [SVC()],\n",
+ " # \"clf__C\": np.logspace(0, 4, 3),\n",
+ " # \"clf__kernel\": [\"poly\"],\n",
+ " # \"clf__degree\": [3],\n",
+ " # \"clf__class_weight\": [None, \"balanced\"],\n",
+ " # }\n",
+ " # \"clf__base_estimator\": [#LogisticRegression(class_weight=\"balanced\",\n",
+ " # #max_iter=1500, C=0.18, solver=\"saga\")],\n",
+ " # SVC(class_weight=\"balanced\", kernel=\"poly\")],\n",
+ " # \"clf__n_estimators\": [30],\n",
+ " # \"clf__learning_rate\": [0.1, 1],\n",
+ " # \"clf__algorithm\": [\"SAMME\", \"SAMME.R\"]\n",
+ "\n",
+ "default_pipe = Pipeline(steps=[\n",
+ " (\"scaler\", PowerTransformer()),\n",
+ " (\"dim_reduce\", PCA(n_components=20)),\n",
+ " (\"clf\", LogisticRegression(class_weight=\"balanced\", max_iter=2500, C=0.18, solver=\"saga\"))\n",
+ "])\n",
+ "\n",
+ "param_grid = [\n",
+ " {\n",
+ " \"clf\": [LogisticRegression(class_weight=\"balanced\", max_iter=1500, C=0.18, solver=\"saga\")],\n",
+ " }\n",
+ "]\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "7731d0e8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Fitting 5 folds for each of 1 candidates, totalling 5 fits\n",
+ "CPU times: total: 4min 27s\n",
+ "Wall time: 9min 9s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "run_gridcv = True\n",
+ "n_splits = 5\n",
+ "cv = StratifiedGroupKFold(n_splits=n_splits, shuffle=True)\n",
+ "\n",
+ "\n",
+ "if run_gridcv:\n",
+ " grid_search = GridSearchCV(estimator=default_pipe,\n",
+ " param_grid=param_grid,\n",
+ " cv=cv.split(X, y, groups),\n",
+ " scoring=[\"balanced_accuracy\", \"precision\", \"f1\", \"roc_auc\"],\n",
+ " refit=\"roc_auc\",\n",
+ " error_score=\"raise\",\n",
+ " n_jobs=-1,\n",
+ " verbose=4,\n",
+ " )\n",
+ " grid_clf = grid_search.fit(X, y)\n",
+ "else:\n",
+ " best_bacc = 0\n",
+ " best_roc = 0\n",
+ " best_model = None\n",
+ " mean = {\"b_acc\": 0, \"prec\": 0, \"f1\": 0, \"roc\": 0}\n",
+ " from copy import deepcopy\n",
+ " for i, (train_id, test_id) in enumerate(cv.split(X, y, groups)):\n",
+ "\n",
+ " X_train, X_test = X[train_id], X[test_id]\n",
+ " y_train, y_test = y[train_id], y[test_id]\n",
+ " \n",
+ " model = deepcopy(default_pipe)\n",
+ " model.fit(X_train, y_train)\n",
+ " y_pred = model.predict(X_test)\n",
+ " \n",
+ " tmp = get_metrics(y_test, y_pred)\n",
+ " \n",
+ " print(i+1, end=\"\")\n",
+ " for k, v in tmp.items():\n",
+ " mean[k] += v\n",
+ " print(f\"\\t{k}: {round(v, 3)}\", end=\"\")\n",
+ " print()\n",
+ " \n",
+ " if best_roc < tmp[\"roc\"]:\n",
+ " best_bacc = tmp[\"b_acc\"]\n",
+ " best_roc = tmp[\"roc\"]\n",
+ " best_model = model\n",
+ " else:\n",
+ " del model\n",
+ " print(\"mean\", end=\"\")\n",
+ " for k, v in mean.items():\n",
+ " print(f\"\\t{k}: {round(v/n_splits, 3)}\", end=\"\")\n",
+ " print()\n",
+ " #refit the best model on the entire dataset\n",
+ " best_model.fit(X, y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "2e3664dc-048f-47d3-af19-f50045c220ec",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=20)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.18, class_weight='balanced', max_iter=1500,\n",
+ " solver='saga')</pre></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grid_clf.best_estimator_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "6dc4ce7e-4664-495e-9621-6d5c9e10022e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>rank_test_balanced_accuracy</th>\n",
+ " <th>mean_test_balanced_accuracy</th>\n",
+ " <th>mean_test_f1</th>\n",
+ " <th>mean_test_roc_auc</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>1</td>\n",
+ " <td>0.687942</td>\n",
+ " <td>0.069266</td>\n",
+ " <td>0.744352</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " rank_test_balanced_accuracy mean_test_balanced_accuracy mean_test_f1 \\\n",
+ "0 1 0.687942 0.069266 \n",
+ "\n",
+ " mean_test_roc_auc \n",
+ "0 0.744352 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "if run_gridcv:\n",
+ " tmp = pd.DataFrame(grid_clf.cv_results_)\n",
+ "tmp[[\"rank_test_balanced_accuracy\", \"mean_test_balanced_accuracy\", \"mean_test_f1\", \"mean_test_roc_auc\"]].sort_values([\"rank_test_balanced_accuracy\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "854649cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pickle\n",
+ "if run_gridcv:\n",
+ " acc = round(max(grid_clf.cv_results_[\"mean_test_balanced_accuracy\"]), 2)\n",
+ " roc = round(max(grid_clf.cv_results_[\"mean_test_roc_auc\"]), 2)\n",
+ " usample_name = str(undersample.__class__).split(\".\")[-1][:-2]\n",
+ " algo_name = str(grid_clf.best_estimator_.steps[2][1].__class__).split(\".\")[-1][:-2]\n",
+ " filename = f\"../models/{usample_name}_best_model_{first_n}_{algo_name}_{acc}_{roc}.pkl\"\n",
+ " pickle.dump(grid_clf.best_estimator_, open(filename, 'wb'))\n",
+ "else:\n",
+ " acc = round(mean[\"b_acc\"]/n_splits, 2)\n",
+ " roc = round(mean[\"roc\"]/n_splits, 2)\n",
+ " algo_name = str(best_model.steps[2][1].__class__).split(\".\")[-1][:-2]\n",
+ " filename = f\"../models/best_model_{first_n}_{algo_name}_{acc}_{roc}.pkl\"\n",
+ " pickle.dump(best_model, open(filename, 'wb'))\n",
+ " best_model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d966f427-46ab-4768-86a7-2eb4a022a672",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/preprocessing.ipynb b/notebooks/preprocessing.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..34ffa12342fc9db5cc07082660462a497b853a22
--- /dev/null
+++ b/notebooks/preprocessing.ipynb
@@ -0,0 +1,156 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "9a431c5d",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "TRAIN_DIR = \"../project_dataset/partial_dataset_train\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "19d24cf4",
+ "metadata": {},
+ "source": [
+ "## Grouping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "a7f64639",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if False:\n",
+ " complete_labels_df = pd.read_csv(f\"{TRAIN_DIR}/complete_labels.csv\", index_col=0)\n",
+ " complete_labels_df = complete_labels_df[:30000]\n",
+ " complete_labels_df[\"group_id\"] = complete_labels_df.astype(bool).groupby(complete_labels_df.columns.tolist(), sort=False).ngroup() + 1\n",
+ " min_ = complete_labels_df[\"group_id\"].min()\n",
+ " max_ = complete_labels_df[\"group_id\"].max()\n",
+ "\n",
+ " def f(r):\n",
+ " if r[\"label\"] == False:\n",
+ " r[\"group_id\"] = np.random.randint(min_, max_, size=1)[0]\n",
+ " return r[\"group_id\"]\n",
+ "\n",
+ " complete_labels_df[\"group_id\"] = complete_labels_df[[\"label\", \"group_id\"]].apply(f, axis=1)\n",
+ " complete_labels_df[\"group_id\"].value_counts()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "id": "d3edae54-82be-406d-a688-15501e17bfd6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.image.AxesImage at 0x1f872acf850>"
+ ]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# plt.spy(complete_labels_df.iloc[15000:15200,0:500])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "200f338e-f2ad-4da1-80cc-ed41f2353125",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.feature_selection import SelectKBest, SelectPercentile"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "16de5abc-3210-4a62-b0c8-8a929ee79d78",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X_ = pd.read_csv(f\"{TRAIN_DIR}/features.csv\", index_col=0)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "c4bba549-6b71-4e33-952b-08254152e9d8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "X = X_[:30_000]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "fe054fd6-c8ac-49c0-a2d1-7d3a3ed490a2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "del X_"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a3b0db58-bf2a-4b11-87cc-11911004f641",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.pipeline import Pipeline\n",
+ "\n",
+ "pipe = Pipeline(steps=[\n",
+ " (\"scaler\", StandardScaler()),\n",
+ " (\"lr_dim_reduce\", PCA(n_components=n_components)),\n",
+ ")"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/val-evaluation.ipynb b/notebooks/val-evaluation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e4de08630198a9d9bf4ae48112172a540f9a6d90
--- /dev/null
+++ b/notebooks/val-evaluation.ipynb
@@ -0,0 +1,285 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "c697b9d5-89eb-4260-a9d9-800921ce0ad2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import pickle\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "import seaborn as sns\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ " \n",
+ "\n",
+ "from sklearn.preprocessing import LabelEncoder\n",
+ "from sklearn.metrics import balanced_accuracy_score, precision_score, roc_auc_score, f1_score, RocCurveDisplay\n",
+ "from sklearn.metrics import confusion_matrix, classification_report"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "5c2a8c29-e60d-481f-b2e3-426849837a2e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "CPU times: total: 1min 58s\n",
+ "Wall time: 2min\n"
+ ]
+ }
+ ],
+ "source": [
+ "%%time\n",
+ "\n",
+ "DATA_DIR = \"../project_dataset\"\n",
+ "VALID_DIR = f\"{DATA_DIR}/partial_dataset_valid\"\n",
+ "\n",
+ "X = pd.read_csv(f\"{VALID_DIR}/features.csv\", index_col=0)\n",
+ "y = pd.read_csv(f\"{VALID_DIR}/labels.csv\", index_col=0)\n",
+ "\n",
+ "X = X.values\n",
+ "y = LabelEncoder().fit_transform(y.values.squeeze())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "cb6d494e-450a-4317-9334-714fd038e20d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(668582, 952)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "X.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ef290b99-fc1e-40bf-8f00-5369ab5be520",
+ "metadata": {},
+ "source": [
+ "## Be sure to delete old the model files before running"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "a5fecb38-b6b2-4e1c-b35e-a7ac87898fba",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_cf_mat(y_true, y_pred, ax):\n",
+ " \n",
+ " print(classification_report(y_true, y_pred))\n",
+ " cf_mat = confusion_matrix(y_true, y_pred)\n",
+ " df_cm = pd.DataFrame(cf_mat, index=[\"False\", \"True\"], columns=[\"False\", \"True\"])\n",
+ " \n",
+ " return sns.heatmap(df_cm, annot=True, ax=ax)\n",
+ "\n",
+ "def get_metrics(y_true, y_pred):\n",
+ " \n",
+ " return {\n",
+ " \"b_acc\": round(balanced_accuracy_score(y_true, y_pred), 2),\n",
+ " \"prec\": round(precision_score(y_true, y_pred), 2),\n",
+ " \"f1\": round(f1_score(y_true, y_pred), 2),\n",
+ " \"roc\": round(roc_auc_score(y_true, y_pred), 2)\n",
+ " }\n",
+ "\n",
+ "def get_roc_curve(model, X, y, title, fig=None):\n",
+ " if fig:\n",
+ " return RocCurveDisplay.from_estimator(model, X, y, name=title, ax=fig.ax_)\n",
+ " return RocCurveDisplay.from_estimator(model, X, y, name=title)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "97ea3e44-49c1-474c-962e-a823e15b962f",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MODEL_DIR = \"../models\"\n",
+ "# best_model_name = \"\"\n",
+ "# best_acc = 0\n",
+ "# y_pred_best = None\n",
+ "# for fname in tqdm(os.listdir(MODEL_DIR)):\n",
+ "# if \"logistic\" in fname.lower() and fname.endswith(\"pkl\"):\n",
+ " \n",
+ "# model = pickle.load(open(f\"{MODEL_DIR}/{fname}\", 'rb'))\n",
+ "# y_pred = model.predict(X)\n",
+ "# d = get_metrics(y, y_pred)\n",
+ " \n",
+ "# if best_acc < d[\"roc\"]:\n",
+ "# best_acc = d[\"roc\"]\n",
+ "# best_model_name = fname\n",
+ "# best_y_pred = y_pred\n",
+ " \n",
+ "# best_model_name, best_acc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "d2a46586-1ba9-4d7b-ae81-5b43e251c940",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model = pickle.load(open(f\"../models/OneSidedSelection_best_model_250000_LogisticRegression_0.7_0.76.pkl\", 'rb'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "039a9d2e-d3f8-44bb-9878-29b08361baa7",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PowerTransformer</label><div class=\"sk-toggleable__content\"><pre>PowerTransformer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PCA</label><div class=\"sk-toggleable__content\"><pre>PCA(n_components=20)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=0.18, class_weight='balanced', max_iter=1500,\n",
+ " solver='saga')</pre></div></div></div></div></div></div></div>"
+ ],
+ "text/plain": [
+ "Pipeline(steps=[('scaler', PowerTransformer()),\n",
+ " ('dim_reduce', PCA(n_components=20)),\n",
+ " ('clf',\n",
+ " LogisticRegression(C=0.18, class_weight='balanced',\n",
+ " max_iter=1500, solver='saga'))])"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "5938edc1-09d7-43ff-9e20-03907ee7b9c1",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'b_acc': 0.72, 'prec': 0.05, 'f1': 0.09, 'roc': 0.72}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# fig = None\n",
+ "# fig, _ = plot_roc(model1, X, y, \"\", fig)\n",
+ "# fig, _ = plot_roc(model2, X, y, \"\", fig)\n",
+ "# plt.show()\n",
+ "y_pred = model.predict(X)\n",
+ "# y_pred2 = model2.predict(X)\n",
+ "d = get_metrics(y, y_pred)\n",
+ "# d2 = get_metrics(y, y_pred2)\n",
+ "# d1[\"roc\"], d2[\"roc\"]\n",
+ "d"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "0830d9e3-5594-4044-a30a-8c5df3e8db45",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ " precision recall f1-score support\n",
+ "\n",
+ " 0 0.99 0.68 0.81 654691\n",
+ " 1 0.05 0.77 0.09 13891\n",
+ "\n",
+ " accuracy 0.68 668582\n",
+ " macro avg 0.52 0.72 0.45 668582\n",
+ "weighted avg 0.97 0.68 0.79 668582\n",
+ "\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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bf5b1h0+zZNsJzuXkX7CuQ8MatG/gQ49Qf82AkkpNTSkRERF7YeBG53fccQc7d+60GRsyZAg33HADzz33HPXr18ff35+YmBhat24NQG5uLqtWreL1118HoE2bNri4uBATE0O/fv0AiI+PZ9euXbzxxhsAhIeHk5aWxsaNG7n55psB2LBhA2lpadbGVXh4OJMmTSI+Pt7aAFuxYgUmk8naNAsPD+eFF14gNzcXV1dXa01AQECxZX0iIiLl5WRqFt/+cZK3fzpIVl7BRes83ZwJ9Hbnxd4hhDeooVlQIv9PTSkRERHB09OT0NBQmzEPDw9q1KhhHR89ejSTJ0+mUaNGNGrUiMmTJ1OlShUiIiIAMJvNDB06lDFjxlCjRg2qV6/O2LFjad68ufVpfE2bNqVHjx5ERkby/vvvA/Doo4/Su3dvmjRpAkC3bt0ICQlh4MCBvPnmm5w5c4axY8cSGRmJl5cXABEREUycOJHBgwfzwgsvcPDgQSZPnsyLL76ob/RFRKTcnErPZs3BZH7Zl8jSnRffE6pnqD/ncvIJqeXF010b4+bidA1Tilw/1JQSERGxExbLxX/Cag+effZZsrKyGD58OCkpKbRt25YVK1bg6elprZk2bRrOzs7069ePrKws7rjjDubPn4+T01/fjC9YsIBRo0ZZn9LXt29fZs6caT3v5OTE0qVLGT58OB06dMDd3Z2IiAimTJlirTGbzcTExDBixAjCwsKoVq0aUVFRxfbPEhERuVIWi4Udx9OY8ctBVh9IJrfg4jOag3086BdWh4fDg/Aw6Z/ZIiXlYDm/K6jB8pIPGx1BpML4vdlzRkcQqTA6nfrimr1X1sr/lvk93Ts9Uub3lIovPT0ds9lMWlqadXaaiEhlkJCWzTfbTzB3zRESz+ZctK57Mz8GtAuidd1qVFUTSqSYkn4voa8eERERERERqZS2x6WyYncCy3cnYHJ2Ym98erGa2tXcaR5oplmAF31bBlKnuruWiouUETWlRERE7IXFuI3ORUREKoOMnHwWbz3Ook1x7D5ZvAEF4OLkQNNaXjx6W326NPXTflAi5UhNKREREREREamwCgst/LwvkWU741my7cQFa0bd3hBzFVd6hvoT4O1+jROKVF5qSomIiNiLQs2UEhERuVp5BYVsj0tl+a4Efv8zmX0JZ4vV1K1ehSc7N6RHc3+83FwMSCkioKaUiIiI/dDyPRERkSty+lwOH/x2mO+2n+RkWvYFazxNzrx5f0u6N/PTnlAidkJNKREREREREbmuFBRaWPNnMr8dSOKzjbFk5hYUq6npaaJHM39uv8GXDg19cHV2NCCpiFyKmlIiIiL2Qsv3RERELqqw0ML6w6d5/qudxJ7JvGBNy9pm7r2xNg/eXFdNKJHrgJpSIiIiIiIiYrcSz2Yz8bs9/LTnFDn5tj/A8TQ507FJTR7v2IDQQLNBCUXkSqkpJSIiYi+0p5SIiAgAOfkFfLH5OHN+O8yx07azohr6VuW5HjfQuUlNnJ00G0rkeqamlIiIiL3Q8j0REanELJaifaI+WnuMn/aeKna+ltmNFU/fhqeelidSYagpJSIiIiIiIoZIOpvD/oSz/PZnEu+vOlzs/K2NfHj0tvrc0tBHT8wTqYDUlBIREbEXmiklIiKVwJZjZ4jZk8h7qw5d8HzTWl483/MGbmukRpRIRaemlIiIiIiIiJSrwkILn22MZfKyvWTmFhQ7f2sjH7o18+euVgF4aXmeSKWhppSIiIi90EbnIiJSwZzNzuOb7Sd58ZtdFFr+Gq9dzZ2H2gbRu0Ut6lSvYlxAETGUmlIiIiL2Qsv3RESkAji/YflnG2L5YVeCzTlPN2cWPRpOSICXQelExJ6oKSUiIiIiIiJXLTuvgNUHkhi1cBvZebY/aBl5e0Meva2+npwnIjbUlBIREbEXWr4nIiLXmbPZefxv83F+3J3AxiNnbM61rG3muZ43EF6/hjYsF5ELUlNKRERERERESuzAqbM88+UO/ohLveD5e1oHMu7OG/D1dLu2wUTkuqOmlIiIiL3QnlIiImKnCgstxOw9xcjPt5Gbf+G/r95+oBV9WgTg6KhZUSJSMmpKiYiI2Ast3xMRETtisVj443gaqw8kMX/tUc5k5Nqcf6Z7E+5uHUigt7tBCUXkeqemlIiIiIiIiFgdPHWWj9cd45P1x4qdc3J0YPQdjXjy9obaJ0pErpqaUiIiIvZCy/dERMRAqw4k8eRnWzmbnV/sXFTXxgwKr4e5ip6eJyJlR00pERERERGRSqqw0MKizXF8tiGWnSfSrOOB3u50aerLqDsaUaOqycCEIlKRqSklIiJiLzRTSkREroHze0XN/OUgm4+lkJqZZz3XsraZSfc0JzTQbGBCEaks1JQSERGxFxaL0QlERKQC23E8lS+3HOfjdcX3ivL3cmPSPaHc0dTPgGQiUlmpKSUiIiIiIlJBxZ7OZPHW47z765/kF9r+8CPQ251OTWry1B2N8PVyMyihiFRmakqJiIjYCy3fExGRMmCxWPhx9ynm/HaYLcdSbM45OMBLvUO458bamN21abmIGEtNKRERERERkQogK7eABRuO8c7PB0n/xxP0bm3kwzPdm9Citrcx4URELkBNKREREXuhmVIiInIFLBYLn26IZXrMAU5n5ALg6uRIaKAXD9xUl/vDauPg4GBwShGR4tSUEhERsRcWNaVERKTk0rPzmLfmKN/tOMmfiecA8HRzZtgt9Xk4PIhqHq4GJxQRuTQ1pURERERERK4jaZl5RP+wl6+2niC34K8faLQJqsZnkW0xOTsZmE5EpOTUlBIREbEXWr4nIiKXEHcmk2k/HeCrrSdsxlvWNjO1fysa1KxqUDIRkSujppSIiIiIiIgdW3somfm/H2XFnlM247c28mH+kJtxctR+USJyfVJTSkRExF5YLEYnEBERO1FYaGH+2qMs2xnP5mMp1nFfTxPDOzVgUPt62rxcRK57akqJiIjYCy3fExER4M/Eczz2yWYOJWVYx3q1qEXPUH96Na+lZpSIVBhqSomIiIiIiNiB1MxcPlh9mFkrD1nH7r0xkKG3BNMswGxgMhGR8qGmlIiIiL3QTCkRkUrpSHIGr36/h9UHk8gr+Gsp9/cjbyE0UM0oEam4HI0OICIiIvZh9uzZtGjRAi8vL7y8vAgPD+eHH36wnh88eDAODg42R7t27WzukZOTw8iRI/Hx8cHDw4O+ffty/Phxm5qUlBQGDhyI2WzGbDYzcOBAUlNTbWpiY2Pp06cPHh4e+Pj4MGrUKHJzc21qdu7cSceOHXF3dycwMJCXX34Zi/blEpHrSOzpTF76Zhfdpq3i532J1obU8z1v4NDkO9WQEpEKTzOlRERE7IXF2JlStWvX5rXXXqNhw4YAfPTRR9x1111s27aNZs2aAdCjRw/mzZtnvcbV1dXmHqNHj+a7775j4cKF1KhRgzFjxtC7d2+2bNmCk5MTABERERw/fpzly5cD8OijjzJw4EC+++47AAoKCujVqxc1a9ZkzZo1nD59mkGDBmGxWJgxYwYA6enpdO3alc6dO7Np0yYOHDjA4MGD8fDwYMyYMeX7iRIRuUpHkzOYvfIQizbHWce8q7gwoU8z7m4daGAyEZFrS00pERERO2EpNHaWT58+fWxeT5o0idmzZ7N+/XprU8pkMuHv73/B69PS0pg7dy6ffPIJXbp0AeDTTz+lTp06/PTTT3Tv3p29e/eyfPly1q9fT9u2bQGYM2cO4eHh7N+/nyZNmrBixQr27NlDXFwcAQEBALz11lsMHjyYSZMm4eXlxYIFC8jOzmb+/PmYTCZCQ0M5cOAAU6dOJSoqSpsAi4hdOpeTT+RHm1l3+LR1zMEBnu1+A0M61MPNxcnAdCIi156W74mIiEgxBQUFLFy4kIyMDMLDw63jK1euxNfXl8aNGxMZGUliYqL13JYtW8jLy6Nbt27WsYCAAEJDQ1m7di0A69atw2w2WxtSAO3atcNsNtvUhIaGWhtSAN27dycnJ4ctW7ZYazp27IjJZLKpOXnyJEePHi3bT4aIyFXKzivgP1/vIuzVGJuG1Bv3teBIdC+e6NRADSkRqZQ0U0pERMRelMNG5zk5OeTk5NiMmUwmm2bO3+3cuZPw8HCys7OpWrUqS5YsISQkBICePXty//33ExQUxJEjR/jPf/7D7bffzpYtWzCZTCQkJODq6kq1atVs7unn50dCQgIACQkJ+Pr6FntfX19fmxo/Pz+b89WqVcPV1dWmpl69esXe5/y54ODgknx6RETK3bbYFO6dvZa/b3k3vX8rLdMTEUFNKRERkQotOjqaiRMn2oy99NJLTJgw4YL1TZo0Yfv27aSmprJ48WIGDRrEqlWrCAkJoX///ta60NBQwsLCCAoKYunSpdx7770XzWCxWGyW011oaV1Z1Jzf5Nwelu6lpaWxZMkSfvvtN44ePUpmZiY1a9akdevWdO/enfbt2xsdUUTKWVpWHp9tiOWNH/dZG1LtG9Tgv4Nv0qwoEZH/d1XL93Jzc9m/fz/5+flllUdERKTyshSW+TFu3DjS0tJsjnHjxl00gqurKw0bNiQsLIzo6GhatmzJ22+/fcHaWrVqERQUxMGDBwHw9/cnNzeXlJQUm7rExETrLCZ/f39OnTpV7F5JSUk2NednRJ2XkpJCXl7eJWvOLyX85yyrayk+Pp7IyEhq1arFyy+/TEZGBq1ateKOO+6gdu3a/Prrr3Tt2pWQkBAWLVpU4vvOmjWL4OBg3NzcaNOmDb/99tsl6xcsWEDLli2pUqUKtWrVYsiQIZw+ffqS14hI2fh1XyIdXvuFlhNX8PryooZU/ZoerH6mM59FtlNDSkTkb66oKZWZmcnQoUOpUqUKzZo1IzY2FoBRo0bx2muvlWlAERGRSqPQUuaHyWTCy8vL5rjY0r0LsVgsxZb/nXf69Gni4uKoVasWAG3atMHFxYWYmBhrTXx8PLt27bLODAoPDyctLY2NGzdaazZs2EBaWppNza5du4iPj7fWrFixApPJRJs2baw1q1evJjc316YmICCg2LK+a6lly5aYzWY2btzI4cOHWbhwIW+99Ravvvoqs2bNYvXq1SQnJ/Of//yHqVOnMmXKlMvec9GiRYwePZrx48ezbds2br31Vnr27Gn9/uuf1qxZw8MPP8zQoUPZvXs3X3zxBZs2bWLYsGFl/eGKyN/sPJ5Go/HLGDJ/EydSs6zjz/W4gZinO1K3RhUD04mI2KcrakqNGzeOP/74g5UrV+Lm5mYd79KlS6l+6iciIiL244UXXrAuN9u5cyfjx49n5cqVPPTQQ5w7d46xY8eybt06jh49ysqVK+nTpw8+Pj7cc889AJjNZoYOHcqYMWP4+eef2bZtGwMGDKB58+bWp/E1bdqUHj16EBkZyfr161m/fj2RkZH07t2bJk2aANCtWzdCQkIYOHAg27Zt4+eff2bs2LFERkbi5eUFQEREBCaTicGDB7Nr1y6WLFnC5MmTDX/y3u7du5kyZQqhoaEXrXF3d+fBBx9kw4YNDBo06LL3nDp1KkOHDmXYsGE0bdqU6dOnU6dOHWbPnn3B+vXr11OvXj1GjRpFcHAwt9xyC4899hibN2++4o9LRC4uO6+Ad3/9kz4z15BXULROL6hGFabc35I/J/XkiU4NcHI0flmxiIg9uqI9pb7++msWLVpEu3btbL7xCwkJ4dChQ2UWTkREpFIph43OS+PUqVMMHDiQ+Ph4zGYzLVq0YPny5XTt2pWsrCx27tzJxx9/TGpqKrVq1aJz584sWrQIT09P6z2mTZuGs7Mz/fr1IysrizvuuIP58+fj5PTXcpUFCxYwatQo61P6+vbty8yZM63nnZycWLp0KcOHD6dDhw64u7sTERFhM6vIbDYTExPDiBEjCAsLo1q1akRFRREVFXUNPlMXV7NmzTKtz83NZcuWLTz//PM24926dbM+rfCf2rdvz/jx41m2bBk9e/YkMTGRL7/8kl69epUqm4hc3k97TjHmiz9Iy8oDwOTsyPLRtxHs42FwMhGR68MVNaWSkpIu+OScjIwMu9hcVEREREpv7ty5Fz3n7u7Ojz/+eNl7uLm5MWPGDGbMmHHRmurVq/Ppp59e8j5169bl+++/v2RN8+bNWb169WUz2ZuUlBS+++47Hn744cvWJicnU1BQUGyfrL8/0fCf2rdvz4IFC+jfvz/Z2dnk5+fTt2/fS/6e/PMpjenp6SX8aEQqp+1xqUR+vJmks0VfN55uzjzbvQkDw+sZG0xE5DpzRcv3brrpJpYuXWp9fb4RNWfOHMLDw8smmYiISGVTWFj2h9id2NhYhgwZUqprLvSkwYv9IHDPnj2MGjWKF198kS1btrB8+XKOHDnC448/ftH7R0dHYzabrUedOnVKlU+kssjKLWDCt7u5b/Zaa0OqXf3qbBrfRQ0pEZErcEUzpaKjo+nRowd79uwhPz+ft99+m927d7Nu3TpWrVpV1hlFREQqh/PPDJfr2uVmGZ09e7bE9/Lx8cHJyemCTxq82FMGo6Oj6dChA8888wwALVq0wMPDg1tvvZVXX33VujH9340bN85m6WN6eroaUyJ/k56dxxebjzMt5gDncoqePN6gpgcv9WnGbY1Lt2xXRET+ckVNqfbt2/P7778zZcoUGjRowIoVK7jxxhtZt24dzZs3L+uMIiIiItcNb2/vS25ncKlZTv/k6upKmzZtiImJsW4oDxATE8Ndd911wWsyMzNxdrb9Fu/8nl6WizQ+TSZTqZ7KKFJZ7E84y/SfDvDDLtvGcFTXxozo3FAbmIuIXKUrakpB0T4OH330UVlmERERqdy03K5C8PT0ZPz48bRt2/aC5w8ePMhjjz1W4vtFRUUxcOBAwsLCCA8P54MPPiA2Nta6HG/cuHGcOHGCjz/+GIA+ffoQGRnJ7Nmz6d69O/Hx8YwePZqbb76ZgICAq/8ARSoBi8XCnN8OM3nZPpvxJzo14PHbGmCu4mJQMhGRiuWKmlJbt27FxcXFOivqm2++Yd68eYSEhDBhwgRcXV3LNKRc3pyPF/H2+/MZcP9dPD/64ntGnLd1x26GPPksDYPrsfijd8s124FDR5g8dRY79xzA7OXJ/Xf15PEhEdafEm/cuoNHRj5X7LpvP/uA+kFaOiDXRt1Rd+NzZ1uqNAqkMDuX9E37OfTKArIOnbzoNa6+3jSYOAjPFvVxr+/PiQ9/4M//zC/3rB5N69Jo8lA8WzckP/UcJz+O4djUL63nvduH0GrJxGLXbezwFJl/XvzjEZGyceONNwLQsWPHC5739va+6IylC+nfvz+nT5/m5ZdfJj4+ntDQUJYtW0ZQUBAA8fHxxMbGWusHDx7M2bNnmTlzJmPGjMHb25vbb7+d119//So+KpHKI/lcDo9+vJmtsanWsVG3N+Tpro31UCcRkTJ2RU2pxx57jOeff57mzZtz+PBh+vfvz7333ssXX3xBZmYm06dPL+OYcik79+7ny29/oHHD4BLVnz2XwQuvTKFtm1acPpN6Ve99Iv4U3e8bzK7ff7jg+XMZGUSOHs/NN7Zg4dy3ORp7gn9Pegt3dzcGP/gvm9rvP59DVY8q1tfVvM1XlU2kNLzDm3Fy3o+kb/8TBycn6r/wIC0X/ZuNtz1NYWbOBa9xNLmQdzqdY9MXU/ux3mWSw61OTdptnsVKv/sveN6pqjst//cfUn7fxcEez+NevxY3vDOCgsxsjr9n+6SyDeGjKDibZX2de1pP07J7hdpTqiKIiIggKyvrouf9/f156aWXSnXP4cOHM3z48Auemz9/frGxkSNHMnLkyFK9h4jA2j+TGf7ZVlIz8wB4ODyIF+5sipuLk8HJREQqpitqSh04cIBWrVoB8MUXX9CxY0c+++wzfv/9dx544AE1pa6hzMwsnp/4JhOee4r3P/q8RNdMfOMdenXtjKOTI7+sXlfs/JKlK/jvgi85EZ9AoL8fD91/Fw/ce2X/4P5+xa/k5uYyaXwUrq6uNKpfj2NxJ/h44RIGPXCvzU+bqlfzxsuz6hW9j8jV2vHgJJvX+56aRYc9c/FsUZ+09XsveE12XBJ//nseALUevP2i9/Z/oBN1RtyFe11fsuOSOP7hMk7OX3FFOf3+dSuOJhf2jXoXS24+GfviiG0QQJ3H+xRrSuUlp5GfnnlF7yMGsWj5XkUQGRl5yfN+fn6lbkqJSPk6mZrFf77exc/7EgFwdXLk/Yfb0LmJr8HJREQqNscruchisVD4//te/PTTT9x5550A1KlTh+Tk5LJLJ5f16lvvclv4TYTf1LpE9UuWriDuRDxPPPLQBc9/+e0PvPP+R4x6dBDfLviAUY8NZsacj/lmWcwV5ftj1z7CWjW3WdLZoe2NJCaf5kT8KZva+4c8Sae+EQwd9Twbt/xxRe8nUlacPYtm7eWnnruq+9QacAfB4x7kSPTnbLz1aQ5P/ozg5x7Ar9+Fl/VcjldYY1LX7cGSm28dO/Prdky1quNW1/Yb5zY/v0n4jg9o+eWLeHdodlUfh4iISEWUlpXHi9/sov1rv1gbUgDfPNlBDSkRkWvgimZKhYWF8eqrr9KlSxdWrVrF7NmzAThy5MhFH08sZW/ZTyvZe+AQCz98u0T1x+JOMG32PD6e9SbOzheegvze/M95ZmQkXTt1AKB2gD+Hj8byv29+4K47u5Y6Y/LpMwTWsv0zUaNataJzZ1KoHeBPzRrVmfDcKEKaNCI3L4/vlv/M0KfGMW/m64S10tMcxRgNXh5E6vq9ZOyLu6r7BD19H4cmfEzyso0AZMcm4tGkNgEPd+XU/1aV+n6uvt5kxyXZjOUmpf11LjaRnFOp7B/zHmf/OIyjyQW/+26j5Zcvsv2eCRed9SV2Qsv3RESumfdXHWL2qkPWpXomZ0de+1dz7mld2+BkIiKVxxU1paZPn85DDz3E119/zfjx42nYsCEAX375Je3bt7/s9Tk5OeTk2O7R4piTo0cRl0L8qSRem/4+H0ybhMl0+Y3lCwoKeHbC64wYOoB6dS/8F+2ZlFQSTiXxYvR0Xnr9bZtrq3p4WF/f9dBjnDz1/z9J+v+NWm/q8tdjqgP8fPlmwfvW1//cENJC0TXnR4ODahMc9FemVqFNSUhMYv5ni9WUEkM0ih5K1aZ12db3P1d1H5caXrjV9qHJ1Cdo8tZfDyBwcHIk/+xfy+puWjUVtzo1//9k0f/devgT6/nsuCQ2dYz668b/3CD5/NfY/49nHTpps0F7+uYDmAJrUGd4XzWlRESk0tt9Mo3HP91C3Jm/9n575a5mDGgXpI3MRUSusStqSrVo0YKdO3cWG3/zzTdxcrr8JoDR0dFMnGj7ZKh/PzOKF5996kriVEp79h/kTEoq/Yf+tYlpQUEhW7bv4vOvvmPrr9/a/F5kZGaxe99B9h08xORpswAoLLRgsVhoeVsvPpg2iQbBRU/xmfDcKFo0u8Hm/Rwd/1rpOfutl8nPLwDgVFIyQ558jsXz/3qC399nYfnUqE7y6RSbe51JSQWgRvVqF/34WjS7ge9//LVEnwuRstRw8iPU6B7G9rtfIif+zNXdzLHoG9v9Y9/j7JY/bU5ZCv/aO2jHQ5NxdC76z7Frreq0/noim29/xnq+MP+vpXq5iam4+nrb3MvVx6vo3P/PmLqQ9C0H8fvXrVf2ccg18/c/FyIiUrbO5eTz5vJ9fLTumHWsfk0PFgxrSy2zu4HJREQqrytqSl2Mm5tbierGjRtHVFSUzZjj2RNlGaXCa9emFUs+mW0z9u9JUwkOqsPQAfcXaw5W9ahSrH7hV9+zccsfTJ00nsBa/lRxd8OvZg2On0ygd/eLb9oc4P/Xcrzz71O3dsAFa1uG3sA7739EXl4eLi4uAKzduBVfnxrFlvX93b4Dh6hZo/pFz4uUh0aTh+Jz581sv+clsmMTL3/BZeQlpZFz8jTudf1IXLzmonU5x//ai89SUNTwzTqacMHa9M0HCH7hQRxcnLHkFTWrqnVqSU78mUtm9gwNJjcx9Qo+CrmmtHxPRKRcbDxyhme//IOjp4tmKreq482U+1vS0FcP2RERMVKJm1LVqlUr8XTWM2cuPbvAZDIVW6qXl6sN0kvDw6MKjerXsxlzd3fD28vTOj5t9jwSk08T/Z+xODo6FquvXs3b+kS88554ZACvTX8PD48q3NoujNy8PHbvO0j62XMMeuDeUufs1bUzs//7GeMnTSXy4f4cizvBnI8X8fiQCOufp08WLSGglh8Ng4PIy8vnux9/IWbl70yb9O9Sv5/IlWr02jD87r2FnYPeoOBcNq41vQHIP5tJYXYuAMHjIzD5V2ffyJnW66o2qweAk4cbLjW8qNqsHoV5+WQeOA7A0Sn/o+Grj5B/LoszP2/D0eSCZ8v6OJurcvx926fllcSpr9ZQb+z93PDOCGLf/gr34FoEPXUvR9/6wlpT+9E7yY5LImNfHA6uzvjddxs1+7Rj15A3r/CzIyIicn06l5PPi1/v4qttRT8A93RzZvydTel/Ux0t1RMRsQMlbkpNnz69HGNIeUg+fYb4U6Wb7XFf3x64u5mY99mXTJ01F3c3Nxo3qMeAfndfUQbPqh7MmT6JSW/Nov/QUXh5VuXhB+61aXDl5eczZeaHJCadxmRypWFwELPenMht7W++ovcUuRKBQ7oD0Ppr26XF+0a9S8KilQCYfKvhFuhjcz7sl78aPZ6tGuD3r1vJjk1k/U0jAIhf8AsFWbnUGd6XBv8ZQEFmDhl7Yzn+wdIryllwNpM/+r1Co+ihtPnxNfLSMoh77zuOv/dXg8vBxZkGLz2Mq391CrNzydgfx46IyZz5edsVvadcQxYt36toOnfuTFBQEPPnz7eODRo0iLi4OH755RfjgolUAiv3J/LkZ9s4l1M0s/j2G3x57V/N8fUs2eoOEREpfw4Wyz93zDVGXvJhoyOIVBi/N3vO6AgiFUanU19cvqiMZLw6oMzv6fHvT8v8nlJyQ4YMoVatWkyePNk69sILLxAfH8+8efMMTHZp6enpmM1m0tLS8PLyMjqOSKnNXXOEV77fY309pmtjRt7RyMBEIiKVS0m/l7jqPaWysrLIy8uzGdM3LyIiIldAe0pVOBdqPP29QSUiZSstM49xS3awbGfR3ow+VV1Z9tStmh0lImKnrqgplZGRwXPPPcf//vc/Tp8+Xex8wf9v1CsiIiKloKfviYhcsSXbjjPxuz2kZhb9wLyJnyfLnroVJ0ftHSUiYq+uqCn17LPP8uuvvzJr1iwefvhh3n33XU6cOMH777/Pa6+9VtYZRURERK4L77zzTolrR40aVY5JRCqP1Mxcnl+8k+W7i2ZHOTs6MOPB1vRsXsvgZCIicjlX1JT67rvv+Pjjj+nUqROPPPIIt956Kw0bNiQoKIgFCxbw0EMPlXVOERGRik/L965706ZNK1Gdg4ODmlIiV8lisfDphlim/LiftKyi2VHOjg5s+XdXzFVcDE4nIiIlcUVNqTNnzhAcHAwU7R915swZAG655RaeeOKJsksnIiIich05cuSI0RFEKoXkczmMXridNX8mW8f+0zuERzrUw8FBy/VERK4XjldyUf369Tl69CgAISEh/O9//wOKZlB5e3uXVTYREZHKxVJY9ocYLjc3l/3795Ofn290FJEKYdPRM3R6c6W1IfVweBD7XunB0FuC1ZASEbnOlKopdfjwYQoLCxkyZAh//PEHAOPGjWPWrFmYTCaefvppnnnmmXIJKiIiUuEVWsr+EMNkZmYydOhQqlSpQrNmzYiNjQWK9pLSHpwiV+Z/m+K4/711nMspavIufiKcl+8Kxc3FyeBkIiJyJUrVlGrUqBHJyck8/fTTjBo1iv79+xMSEsK+ffv4/PPP2bp1K0899VR5ZRURERG5bowbN44//viDlStX4ub21+Pou3TpwqJFiwxMJnL9yc4rYPbKQzy7eAcAPlVd+XVsJ9oEVTc4mYiIXI1S7Sllsdj+xHXZsmVER0dTv3596tatW6bBREREKhtLoZbbVSRff/01ixYtol27djZLikJCQjh06JCByUSuLz/uTuDpRdvJzC0AwNPNmTXP3a7ZUSIiFcAVbXQuIiIiIpeWlJSEr69vsfGMjAzteyNSAsdTMhm9cDubj6VYx1648waG3lIfJ0d9DYmIVASlako5ODgU+yZK31SJiIiUEe0BVaHcdNNNLF26lJEjRwJ/fc80Z84cwsPDjYwmYvdWHUhiyLyN1v8sDulQj9F3NMZcxcXYYCIiUqZKvXxv8ODBmEwmALKzs3n88cfx8PCwqfvqq6/KLqGIiEhloaZUhRIdHU2PHj3Ys2cP+fn5vP322+zevZt169axatUqo+OJ2KXM3Hxe/GY3X245bh2bGdGa3i0CDEwlIiLlpVRNqUGDBtm8HjBgQJmGEREREako2rdvz++//86UKVNo0KABK1as4MYbb2TdunU0b97c6HgidsVisfDtHyeZ+N0ezmTkAtClqS9v3d9Ks6NERCqwUjWl5s2bV145RERExKKNziua5s2b89FHHxkdQ8SupWTkMv7rnSzbmWAde+qORjzdtbGBqURE5FrQRuciIiIi5aSgoIAlS5awd+9eHBwcaNq0KXfddRfOzvoWTATg4KmzPLFgK38mnsPRAe5uHchzPW7Az8vN6GgiInIN6DsiERERe6E9pSqUXbt2cdddd5GQkECTJk0AOHDgADVr1uTbb7/VEj6p1PILCpmy4gDvrToEgKebM2/8qwU9m9cyOJmIiFxLakqJiIjYCYuaUhXKsGHDaNasGZs3b6ZatWoApKSkMHjwYB599FHWrVtncEIRY2TnFTDso82s+TMZgAY1Pfhw0E0E+3hc5koREalo1JQSERERKQd//PGHTUMKoFq1akyaNImbbrrJwGQixikotDBk3ibWHT4NwOR7mhPRtq7BqURExChqSomIiNgLzZSqUJo0acKpU6do1qyZzXhiYiINGzY0KJWIcc7l5HPn278ReyYTZ0cH3n3oRro38zc6loiIGEhNKREREZEykp6ebv315MmTGTVqFBMmTKBdu3YArF+/npdffpnXX3/dqIgihsjMzafr1FXEp2UDMPne5mpIiYiImlIiIiJ2o7DQ6ARylby9vXFwcLC+tlgs9OvXzzpmsRTNhuvTpw8FBQWGZBS51v5MPMeTn221NqQ+GNiGbmpIiYgIakqJiIjYDy3fu+79+uuvRkcQsSsbDp+m/wfrra+n92+lhpSIiFipKSUiIiJSRjp27Gh0BBG7sXxXPE8t3G59/erdodzdOtC4QCIiYnccjQ4gIiIi/6/QUvZHKcyePZsWLVrg5eWFl5cX4eHh/PDDD9bzFouFCRMmEBAQgLu7O506dWL37t0298jJyWHkyJH4+Pjg4eFB3759OX78uE1NSkoKAwcOxGw2YzabGThwIKmpqTY1sbGx9OnTBw8PD3x8fBg1ahS5ubk2NTt37qRjx464u7sTGBjIyy+/bF0eZ08yMzPZt28fO3bssDlEKrIvNsfx+KdbyckvpF6NKmz5dxcGtAsyOpaIiNgZNaVEREQEgNq1a/Paa6+xefNmNm/ezO23385dd91lbTy98cYbTJ06lZkzZ7Jp0yb8/f3p2rUrZ8+etd5j9OjRLFmyhIULF7JmzRrOnTtH7969bfZPioiIYPv27Sxfvpzly5ezfft2Bg4caD1fUFBAr169yMjIYM2aNSxcuJDFixczZswYa016ejpdu3YlICCATZs2MWPGDKZMmcLUqVOvwWeqZJKSkujduzeenp40a9aM1q1b2xwiFdXnG2N55suixmu7+tX58enbqFHVZHAqERGxRw4WO/mRYl7yYaMjiFQYvzd7zugIIhVGp1NfXLP3Sn+se5nf0+v9H6/q+urVq/Pmm2/yyCOPEBAQwOjRo3nuuaL/xuTk5ODn58frr7/OY489RlpaGjVr1uSTTz6hf//+AJw8eZI6deqwbNkyunfvzt69ewkJCWH9+vW0bdsWKHoiXXh4OPv27aNJkyb88MMP9O7dm7i4OAICAgBYuHAhgwcPJjExES8vL2bPns24ceM4deoUJlPRP3Zfe+01ZsyYwfHjx202GzfKQw89xNGjR5k+fTqdO3dmyZIlnDp1ildffZW33nqLXr16GR3xotLT0zGbzaSlpeHl5WV0HLmO/G9zHM9++ddMwP2v9sDk7GRgIhERMUJJv5fQTCkRERF7UQ7L93JyckhPT7c5cnJyLhuloKCAhQsXkpGRQXh4OEeOHCEhIYFu3bpZa0wmEx07dmTt2rUAbNmyhby8PJuagIAAQkNDrTXr1q3DbDZbG1IA7dq1w2w229SEhoZaG1IA3bt3Jycnhy1btlhrOnbsaG1Ina85efIkR48evYJPftn75ZdfmDZtGjfddBOOjo4EBQUxYMAA3njjDaKjo42OJ1Km8gsK+c/Xu6wNqVsa+vDnpJ5qSImIyCWpKSUiIlKBRUdHW/duOn9cqiGyc+dOqlatislk4vHHH2fJkiWEhISQkJAAgJ+fn029n5+f9VxCQgKurq5Uq1btkjW+vr7F3tfX19em5p/vU61aNVxdXS9Zc/71+RqjZWRkWD/W6tWrk5SUBEDz5s3ZunWrkdFEylRufiGPfbKFT9YfA8C7igsfP3Izzk76p4aIiFyanr4nIiJiL0q5MXlJjBs3jqioKJuxv88u+qcmTZqwfft2UlNTWbx4MYMGDWLVqlXW8/9cFmexWC67VO6fNReqL4ua8zsS2MPSPSj6XO7fv5969erRqlUr3n//ferVq8d7771HrVq1jI4nUiZOpWdz33triTuTBcCdzf2Z+eCNODrax9ehiIjYNzWlREREKjCTyXTJJtQ/ubq60rBhQwDCwsLYtGkTb7/9tnUfqYSEBJuGSmJionWGkr+/P7m5uaSkpNjMlkpMTKR9+/bWmlOnThV736SkJJv7bNiwweZ8SkoKeXl5NjX/nBGVmJgIFJ/NZZTRo0cTHx8PwEsvvUT37t1ZsGABrq6uzJ8/39hwImXgVHo2Az7cYG1I/btXU4bdWt/gVCIicj3RnFoRERE7YSm0lPlx1ZksRftSBQcH4+/vT0xMjPVcbm4uq1atsjac2rRpg4uLi01NfHw8u3btstaEh4eTlpbGxo0brTUbNmwgLS3NpmbXrl3Whg7AihUrMJlMtGnTxlqzevVqcnNzbWoCAgKoV6/eVX/cZeGhhx5i8ODBALRu3ZqjR4+yadMm4uLirBvBi1yvdp1I4/731nEw8RxOjg68N+BGNaRERKTUNFNKRETEXpTD8r3SeOGFF+jZsyd16tTh7NmzLFy4kJUrV7J8+XIcHBwYPXo0kydPplGjRjRq1IjJkydTpUoVIiIiADCbzQwdOpQxY8ZQo0YNqlevztixY2nevDldunQBoGnTpvTo0YPIyEjef/99AB599FF69+5NkyZNAOjWrRshISEMHDiQN998kzNnzjB27FgiIyOtT2+JiIhg4sSJDB48mBdeeIGDBw8yefJkXnzxRbtZvvdPVapU4cYbbzQ6hshVs1gs/OebXcSeycTT5Mznj7YjNNBsdCwREbkOqSklIiIiAJw6dYqBAwcSHx+P2WymRYsWLF++nK5duwLw7LPPkpWVxfDhw0lJSaFt27asWLECT09P6z2mTZuGs7Mz/fr1IysrizvuuIP58+fj5PTXE7gWLFjAqFGjrE/p69u3LzNnzrSed3JyYunSpQwfPpwOHTrg7u5OREQEU6ZMsdaYzWZiYmIYMWIEYWFhVKtWjaioqGL7Z11rpXn/qVOnlmMSkfLzyvd72RabCsBHQ29WQ0pERK6Yg+X8rqAGy0s+bHQEkQrj92bPGR1BpMLodOqLa/ZeaQPvKPN7mj/5uczvKRfXuXPnEtU5ODjwyy+/lHOaK5eeno7ZbCYtLc06O00E4P1Vh3h9+T4KLTDqjkZEdW1sdCQREbFDJf1eQjOlRERERMrIr7/+anQEkXJhsViY9/tRon/YB0Cv5rXUkBIRkaumppSIiIidKIuNyUVEylpWbgFjv/iDpTuLHj7Qq3ktZka0NjiViIhUBGpKiYiI2As1pUTEzhQWWnj0k838djAZgEc6BPPvXk3t9oECIiJyfVFTSkRERERELmjUwm38djAZFycHpvdvTa8WtYyOJCIiFYiaUiIiIvai0OgAIiJ/+fC3w3y/o2jJ3rieTdWQEhGRMqemlIiIiIiI2Ji8bC8frC56OvY9rQN55JZggxOJiEhF5Gh0ABERESliKbSU+SHG+uSTT+jQoQMBAQEcO3YMgOnTp/PNN98YnEzkwiwWCy9/t8fakOrVohZT+7U0OJWIiFRUakqJiIjYi8JyOMQws2fPJioqijvvvJPU1FQKCgoA8Pb2Zvr06caGE7mIMV/8wX9/PwJAfR8PZj7YWpuai4hIuVFTSkRERKQczJgxgzlz5jB+/HicnJys42FhYezcudPAZCLFnZ8h9dXWEwAMbl+PX8Z2UkNKRETKlfaUEhERsRNablexHDlyhNatWxcbN5lMZGRkGJBI5OKmrNhvnSHVL6w2E/o2MziRiIhUBpopJSIiIlIOgoOD2b59e7HxH374gZCQkGsfSOQC0rPzeOaLP3j310MA9G0ZwBv3aQ8pERG5NjRTSkRExF5oD6gK5ZlnnmHEiBFkZ2djsVjYuHEjn3/+OdHR0Xz44YdGxxPheEom97+3jvi0bACG3hLMf3qrYSoiIteOmlIiIiJ2wqKmVIUyZMgQ8vPzefbZZ8nMzCQiIoLAwEDefvttHnjgAaPjSSWXmZvPiM+2EZ+WTVWTMwPaBfFcjyZGxxIRkUpGTSkRERGRchIZGUlkZCTJyckUFhbi6+trdCQRMnLy6f/BOnadSMfTzZmvR3SgQc2qRscSEZFKSHtKiYiI2IvCcjjEMBMnTuTQoaJ9enx8fNSQEruQV1DIqM+3setEOgBzHg5TQ0pERAyjppSIiIhIOVi8eDGNGzemXbt2zJw5k6SkJKMjSSVXUGhhxIKt/LwvEYAZD7amXf0aBqcSEZHKTE0pERERO2EpLPtDjLNjxw527NjB7bffztSpUwkMDOTOO+/ks88+IzMz0+h4UsnkFRQyfMEWVuw5BcDbD7SiT8sAg1OJiEhlp6aUiIiIvdDyvQqnWbNmTJ48mcOHD/Prr78SHBzM6NGj8ff3NzqaVCK5+YU8Mn8TP+4uaki98a8W3NUq0OBUIiIiakqJiIiIXBMeHh64u7vj6upKXl6e0XGkEnnl+z38djAZgAl9Quh3Ux2DE4mIiBRRU0pERMROaPlexXPkyBEmTZpESEgIYWFhbN26lQkTJpCQkGB0NKkklu6IZ+GmWADeuK8FgzsEG5xIRETkL85GBxARERGpiMLDw9m4cSPNmzdnyJAhREREEBioJVNy7ew6kcaIz7YC0C3Ej35hmiElIiL2RU0pERERO6GZTRVL586d+fDDD2nWrJnRUaQSyszN57nFOwBoWdvMrIduNDiRiIhIcWpKiYiI2Ak1pSqWyZMnGx1BKrEXvtrJ7pPpAEy6pznOTtq1Q0RE7I+aUiIiIiJlJCoqildeeQUPDw+ioqIuWTt16tRrlEoqm/9tjuPr7ScBmDsojNBAs8GJRERELkw/MhEREbEXFoeyP+Sa2rZtm/XJetu2bbvkURqzZs0iODgYNzc32rRpw2+//XbJ+pycHMaPH09QUBAmk4kGDRrw3//+94o/Lrl+zF1zhGe/LFq293B4EHc09TM4kYiIyMVpppSIiIhIGfn1118v+OursWjRIkaPHs2sWbPo0KED77//Pj179mTPnj3UrVv3gtf069ePU6dOMXfuXBo2bEhiYiL5+fllkkfs1yfrj/HK93sAaBNUjRd7hxicSERE5NI0U0pERMROWArL/hDjPPLII5w9e7bYeEZGBo888kiJ7zN16lSGDh3KsGHDaNq0KdOnT6dOnTrMnj37gvXLly9n1apVLFu2jC5dulCvXj1uvvlm2rdvf8Ufi9i/TUfP8OI3uwDo0tSXLx8P1z5SIiJi9/Q3lYiIiJ2wFDqU+SHG+eijj8jKyio2npWVxccff1yie+Tm5rJlyxa6detmM96tWzfWrl17wWu+/fZbwsLCeOONNwgMDKRx48aMHTv2glmkYjiZmkXkx5uxWCDYx4N3H7oRBwd9/YuIiP3T8j0RERGRMpSeno7FYsFisXD27Fnc3Nys5woKCli2bBm+vr4luldycjIFBQX4+dnuC+Tn50dCQsIFrzl8+DBr1qzBzc2NJUuWkJyczPDhwzlz5sxF95XKyckhJyfH5mOQ60NeQSHPfPkHqZl5uLk48vWIDpicnYyOJSIiUiJqSomIiNgJLberGLy9vXFwcMDBwYHGjRsXO+/g4MDEiRNLdc9/znqxWCwXnQlTWFiIg4MDCxYswGwueura1KlTue+++3j33Xdxd3cvdk10dHSpM4l9mPDtbn7/8zTOjg589UQHzO4uRkcSEREpMTWlRERERMrQr7/+isVi4fbbb2fx4sVUr17des7V1ZWgoCACAgJKdC8fHx+cnJyKzYpKTEwsNnvqvFq1ahEYGGhtSAE0bdoUi8XC8ePHadSoUbFrxo0bR1RUlPV1eno6derUKVFGMc7R5AwWbIgFYHD7eoQEeBmcSEREpHTUlBIREbETFov2gKkIOnbsCMCRI0eoW7fuVe3t4+rqSps2bYiJieGee+6xjsfExHDXXXdd8JoOHTrwxRdfcO7cOapWrQrAgQMHcHR0pHbt2he8xmQyYTKZrjinXHvp2Xk8sWArAD5VTYzv1dTgRCIiIqWnppSIiIid0PK969+OHTsIDQ3F0dGRtLQ0du7cedHaFi1alOieUVFRDBw4kLCwMMLDw/nggw+IjY3l8ccfB4pmOZ04ccK6eXpERASvvPIKQ4YMYeLEiSQnJ/PMM8/wyCOPXHDpnlyfopftY298Ol5uzix+Ilwbm4uIyHVJTSkRERGRMtKqVSsSEhLw9fWlVatWODg4YLFYitU5ODhQUFBQonv279+f06dP8/LLLxMfH09oaCjLli0jKCgIgPj4eGJjY631VatWJSYmhpEjRxIWFkaNGjXo168fr776atl8kGK4zzbE8vnGot/zcXc2JaiGh8GJREREroyaUiIiInbCUqiZDte7I0eOULNmTeuvy8rw4cMZPnz4Bc/Nnz+/2NgNN9xATExMmb2/2I9DSed48ZtdADx4cx0evLmuwYlERESunJpSIiIiImXk/Oylf/5apCwUFloYvXA7+YUWAr3dmXxPc6MjiYiIXBVHowOIiIhIEYul7A8xzkcffcTSpUutr5999lm8vb1p3749x44dMzCZXK8++O0wO0+kAfDJ0Ju1j5SIiFz31JQSERGxE5ZChzI/SiM6OpqbbroJT09PfH19ufvuu9m/f79NzeDBg3FwcLA52rVrZ1OTk5PDyJEj8fHxwcPDg759+3L8+HGbmpSUFAYOHIjZbMZsNjNw4EBSU1NtamJjY+nTpw8eHh74+PgwatQocnNzbWp27txJx44dcXd3JzAwkJdffvmCezgZYfLkydaNxdetW8fMmTN544038PHx4emnnzY4nVxvVuxO4LUf9gEwuksj6tesanAiERGRq6emlIiIiACwatUqRowYwfr164mJiSE/P59u3bqRkZFhU9ejRw/i4+Otx7Jly2zOjx49miVLlrBw4ULWrFnDuXPn6N27t83G3hEREWzfvp3ly5ezfPlytm/fzsCBA63nCwoK6NWrFxkZGaxZs4aFCxeyePFixowZY61JT0+na9euBAQEsGnTJmbMmMGUKVOYOnVqOX2GSicuLo6GDRsC8PXXX3Pffffx6KOPEh0dzW+//WZwOrmeHEnOYMz//gCgY+OaPHVHI4MTiYiIlA3tKSUiImInjN7ofPny5Tav582bh6+vL1u2bOG2226zjptMJvz9/S94j7S0NObOncsnn3xCly5dAPj000+pU6cOP/30E927d2fv3r0sX76c9evX07ZtWwDmzJlDeHg4+/fvp0mTJqxYsYI9e/YQFxdHQEAAAG+99RaDBw9m0qRJeHl5sWDBArKzs5k/fz4mk4nQ0FAOHDjA1KlTiYqKMnxpU9WqVTl9+jR169ZlxYoV1tlRbm5uZGVlGZpNrh+FhRae/GwrZ3PyqeHhyvsD2xj+Z1tERKSsaKaUiIiIXFBaWtHeNdWrV7cZX7lyJb6+vjRu3JjIyEgSExOt57Zs2UJeXh7dunWzjgUEBBAaGsratWuBoqVsZrPZ2pACaNeuHWaz2aYmNDTU2pAC6N69Ozk5OWzZssVa07FjR0wmk03NyZMnOXr0aBl9Fq5c165dGTZsGMOGDePAgQP06tULgN27d1OvXj1jw8l147ONsew+mY6biyNfDW+Pm4uT0ZFERETKjJpSIiIidqI8NjrPyckhPT3d5sjJySlBFgtRUVHccssthIaGWsd79uzJggUL+OWXX3jrrbfYtGkTt99+u/WeCQkJuLq6Uq1aNZv7+fn5kZCQYK3x9fUt9p6+vr42NX5+fjbnq1Wrhqur6yVrzr8+X2Okd999l/DwcJKSkli8eDE1atQAihp3Dz74oMHp5HqQmZvPpKV7ARh6SzBBNTwMTiQiIlK2tHxPRETETpTH8r3o6GgmTpxoM/bSSy8xYcKES1735JNPsmPHDtasWWMz3r9/f+uvQ0NDCQsLIygoiKVLl3Lvvfde9H4Wi8VmydGFlh+VRc35Tc7tYXmTt7c3M2fOLDb+z98PkYuZ9/tRsvIK8K7iwpOdtY+UiIhUPGpKiYiIVGDjxo0jKirKZuzvy90uZOTIkXz77besXr2a2rVrX7K2Vq1aBAUFcfDgQQD8/f3Jzc0lJSXFZrZUYmIi7du3t9acOnWq2L2SkpKsM538/f3ZsGGDzfmUlBTy8vJsav45I+r8UsJ/zqAySmpqKnPnzmXv3r04ODjQtGlThg4ditlsNjqa2LlVB5J488eip1+O7dYEd1ct2xMRkYpHy/dERETshMXiUOaHyWTCy8vL5rhYU8pisfDkk0/y1Vdf8csvvxAcHHzZzKdPnyYuLo5atWoB0KZNG1xcXIiJibHWxMfHs2vXLmtTKjw8nLS0NDZu3Git2bBhA2lpaTY1u3btIj4+3lqzYsUKTCYTbdq0sdasXr2a3Nxcm5qAgAC72LNp8+bNNGjQgGnTpnHmzBmSk5OZNm0aDRo0YOvWrUbHEzuWfC6HYR9tAqBbiB8Pta1rcCIREZHyoaaUiIiIADBixAg+/fRTPvvsMzw9PUlISCAhIcH6pLhz584xduxY1q1bx9GjR1m5ciV9+vTBx8eHe+65BwCz2czQoUMZM2YMP//8M9u2bWPAgAE0b97c+jS+pk2b0qNHDyIjI1m/fj3r168nMjKS3r1706RJEwC6detGSEgIAwcOZNu2bfz888+MHTuWyMhIvLy8AIiIiMBkMjF48GB27drFkiVLmDx5sl08eQ/g6aefpm/fvhw9epSvvvqKJUuWcOTIEXr37s3o0aONjid2Kr+gkBELtpJXYCHQ250p/VraxZ9nERGR8qDleyIiInbCUmjs+8+ePRuATp062YzPmzePwYMH4+TkxM6dO/n4449JTU2lVq1adO7cmUWLFuHp6WmtnzZtGs7OzvTr14+srCzuuOMO5s+fj5PTX8uPFixYwKhRo6xP6evbt6/N/ktOTk4sXbqU4cOH06FDB9zd3YmIiGDKlCnWGrPZTExMDCNGjCAsLIxq1aoRFRVVbLmiUTZv3sycOXNwdv7r2y1nZ2eeffZZwsLCDEwm9uydnw+y4cgZAKbc3xIvNxeDE4mIiJQfNaVERETsRKHF2NkQ5zcJvxh3d3d+/PHHy97Hzc2NGTNmMGPGjIvWVK9enU8//fSS96lbty7ff//9JWuaN2/O6tWrL5vJCF5eXsTGxnLDDTfYjMfFxdk08UTOO3Y6g3dXHgLgsdvqE96ghsGJREREypeW74mIiIiUg/79+zN06FAWLVpEXFwcx48fZ+HChQwbNowHH3zQ6Hhih/799S4KCi3Ur+nB010bGx1HRESk3GmmlIiIiJ2wGDxTSsrWlClTcHBw4OGHHyY/Px8AFxcXnnjiCV577TWD04m9+WFnPL8dTAZgxoOtcXPR0/ZERKTiU1NKREREpBy4urry9ttvEx0dzaFDh7BYLDRs2JAqVaoYHU3szJ+JZxn7xR8ADG5fj2YBZoMTiYiIXBtaviciImInLIUOZX7ItZeZmcmIESMIDAzE19eXYcOGUatWLVq0aKGGlBRz+lwOQz/aTEZuATf4e/Jcjxsuf5GIiEgFoaaUiIiInbBYyv6Qa++ll15i/vz59OrViwceeICYmBieeOIJo2OJnXor5gDHTmdicnbk02FtcXfVsj0REak8tHxPREREpAx99dVXzJ07lwceeACAAQMG0KFDBwoKCnByUsNB/nI8JZOFG2MBmD3gRnyqmgxOJCIicm1pppSIiIid0PK9iiEuLo5bb73V+vrmm2/G2dmZkydPGphK7E12XgHDF2yl0AI316vO7Tf4GR1JRETkmlNTSkRERKQMFRQU4OrqajPm7OxsfQKfiMVi4d9f72LH8TScHB14rmcToyOJiIgYQsv3RERE7EShRTObKgKLxcLgwYMxmf5aipWdnc3jjz+Oh4eHdeyrr74yIp7Ygflrj/LlluM4OMAHA9vQJqi60ZFEREQMoaaUiIiInbCoKVUhDBo0qNjYgAEDDEgi9uhkahaTlu4F4MGb63JHUy3bExGRyktNKREREZEyNG/ePKMjiJ2yWCy0f+0XAIJ9PHjlrlCDE4mIiBhLTSkRERE7YbEYnUBEytPK/UnWX8+MaI2To2ZHiohI5aaNzkVERETKyOOPP05cXFyJahctWsSCBQvKOZHYi+y8AiZ+txuAlrXNNAswG5xIRETEeJopJSIiYie00fn1r2bNmoSGhtK+fXv69u1LWFgYAQEBuLm5kZKSwp49e1izZg0LFy4kMDCQDz74wOjIco28tWI/R09n4l3FhTmDwoyOIyIiYhfUlBIREbET2uj8+vfKK68wcuRI5s6dy3vvvceuXbtsznt6etKlSxc+/PBDunXrZlBKudZ+3ZfInN+OADCma2N8Pd0MTiQiImIf1JQSERERKUO+vr6MGzeOcePGkZqayrFjx8jKysLHx4cGDRrg4KDmY2WSk1/A+CU7AfD1NDGgXZDBiUREROyHmlIiIiJ2QhudVzze3t54e3sbHUMMNHvlIU6mZQPwydC2akqKiIj8jTY6FxEREREpB1uOneGdnw8CRcv2mvh7GpxIRETEvmimlIiIiJ3QRuciFUd+QSGjPt9OoQU6NanJk7c3NDqSiIiI3bGbppR7wK1GRxCpMJwdnYyOIFJhZF/D99JG5yIVx+Rl+ziRmoW7ixNv3NdCy/ZEREQuQMv3RERERETK0JHkDP77e9HT9kbd0UhP2xMREbkINaVERETsRKHFocwPMVZ+fj4//fQT77//PmfPngXg5MmTnDt3zuBkUl4sFgvPfvkHAPV9PIi8NdjgRCIiIvbLbpbviYiIiFQkx44do0ePHsTGxpKTk0PXrl3x9PTkjTfeIDs7m/fee8/oiFIO3l99mE1HUwB49e5QnJ30M2AREZGL0d+SIiIidsJSDocY56mnniIsLIyUlBTc3d2t4/fccw8///yzgcmkvCSdzWH2ykMA3Ns6kPYNfQxOJCIiYt80U0pERMROaLldxbJmzRp+//13XF1dbcaDgoI4ceKEQamkPE38bjdpWXl4V3HhjftaGB1HRETE7mmmlIiIiEg5KCwspKCgoNj48ePH8fT0NCCRlKeV+xP5fkc8AHMeDtOyPRERkRLQ35YiIiJ2wmJxKPNDjNO1a1emT59ufe3g4MC5c+d46aWXuPPOO40LJmUuO6+AF7/ZDUCrOt7cVK+6wYlERESuD1q+JyIiIlIOpk2bRufOnQkJCSE7O5uIiAgOHjyIj48Pn3/+udHxpAy9v+owsWcyMbu7MOfhMKPjiIiIXDfUlBIREbEThUYHkDIVEBDA9u3bWbhwIVu2bKGwsJChQ4fy0EMP2Wx8Lte33PxC5q45DMCzPZpQ09NkcCIREZHrh5pSIiIidsKClttVJKtXr6Z9+/YMGTKEIUOGWMfz8/NZvXo1t912m4HppKx8uOYw6dn5mJwd+deNtY2OIyIicl3RnlIiIiIi5aBz586cOXOm2HhaWhqdO3c2IJGUtbTMPKbHHARgbLcmuLk4GZxIRETk+qKZUiIiInai0GJ0AilLFosFB4fis99Onz6Nh4eHAYmkLFksFv7zzS5yCwqp7+PBsFuDjY4kIiJy3VFTSkRERKQM3XvvvUDR0/YGDx6MyfTXHkMFBQXs2LGD9u3bGxVPysgn64/x7R8nAXiqS6MLNiBFRETk0tSUEhERsROF2lOqQjCbzUDRTBpPT0+bTc1dXV1p164dkZGRRsWTMpCSkUv0sn0A3NUqgLtaBRqcSERE5PqkppSIiIid0EbnFcO8efMAqFevHmPHjtVSvQro/dWHycorwNXJkbfub2l0HBERkeuWmlIiIiIi5eCll14yOoKUg6SzOXy09igAz/ZogrOTnhskIiJypdSUEhERsROFRgeQMvfll1/yv//9j9jYWHJzc23Obd261aBUcjU+WnuUrLwCPFydeKSDNjcXERG5GvrRjoiIiAAQHR3NTTfdhKenJ76+vtx9993s37/fpsZisTBhwgQCAgJwd3enU6dO7N6926YmJyeHkSNH4uPjg4eHB3379uX48eM2NSkpKQwcOBCz2YzZbGbgwIGkpqba1MTGxtKnTx88PDzw8fFh1KhRxRo7O3fupGPHjri7uxMYGMjLL7+MxWIfjzF85513GDJkCL6+vmzbto2bb76ZGjVqcPjwYXr27Gl0PLkC6dl5zP//WVJjuzfB0VFLbkVERK6GmlIiIiJ2woJDmR+lsWrVKkaMGMH69euJiYkhPz+fbt26kZGRYa154403mDp1KjNnzmTTpk34+/vTtWtXzp49a60ZPXo0S5YsYeHChaxZs4Zz587Ru3dvCgoKrDURERFs376d5cuXs3z5crZv387AgQOt5wsKCujVqxcZGRmsWbOGhQsXsnjxYsaMGWOtSU9Pp2vXrgQEBLBp0yZmzJjBlClTmDp16pV8+svcrFmz+OCDD5g5cyaurq48++yzxMTEMGrUKNLS0oyOJ1dg+KdbOZeTTy2zGw+H1zM6joiIyHXPwWInP050dtVTS0TKirOjk9ERRCqM7OzYa/Zey/0eKPN79ji18IqvTUpKwtfXl1WrVnHbbbdhsVgICAhg9OjRPPfcc0DRrCg/Pz9ef/11HnvsMdLS0qhZsyaffPIJ/fv3B+DkyZPUqVOHZcuW0b17d/bu3UtISAjr16+nbdu2AKxfv57w8HD27dtHkyZN+OGHH+jduzdxcXEEBAQAsHDhQgYPHkxiYiJeXl7Mnj2bcePGcerUKUwmEwCvvfYaM2bM4Pjx4zg4GDuLpUqVKuzdu5egoCB8fX2JiYmhZcuWHDx4kHbt2nH69GlD811Keno6ZrOZtLQ0vLy8jI5jF3adSKP3jDUAzHk4jK4hfgYnEhERsV8l/V5CM6VERETkgs7P5qlevToAR44cISEhgW7dullrTCYTHTt2ZO3atQBs2bKFvLw8m5qAgABCQ0OtNevWrcNsNlsbUgDt2rXDbDbb1ISGhlobUgDdu3cnJyeHLVu2WGs6duxobUidrzl58iRHjx4ty0/FFfH397c2noKCgli/fj1Q9Hm0k58JSinM+OUgAGFB1dSQEhERKSNqSomIiNiJwnI4cnJySE9PtzlycnIum8VisRAVFcUtt9xCaGgoAAkJCQD4+dn+g9zPz896LiEhAVdXV6pVq3bJGl9f32Lv6evra1Pzz/epVq0arq6ul6w5//p8jZFuv/12vvvuOwCGDh3K008/TdeuXenfvz/33HOPwemkNPYnnOXH3acAmNC3mcFpREREKg49fU9ERKQCi46OZuLEiTZjL730EhMmTLjkdU8++SQ7duxgzZo1xc79c1mcxWK57FK5f9ZcqL4sas7PQDJ66R7ABx98QGFh0TMVH3/8capXr86aNWvo06cPjz/+uMHppDSmxRwAoHOTmoQGmg1OIyIiUnGoKSUiImInSrsxeUmMGzeOqKgom7G/L3e7kJEjR/Ltt9+yevVqateubR339/cHimYh1apVyzqemJhonaHk7+9Pbm4uKSkpNrOlEhMTad++vbXm1KlTxd43KSnJ5j4bNmywOZ+SkkJeXp5NzT9nRCUmJgLFZ3MZwdHREUfHvyal9+vXj379+gFw4sQJAgO1n+b1YG98Ost3F/05G3VHI4PTiIiIVCxaviciImInCh3K/jCZTHh5edkcF2tKWSwWnnzySb766it++eUXgoODbc4HBwfj7+9PTEyMdSw3N5dVq1ZZG05t2rTBxcXFpiY+Pp5du3ZZa8LDw0lLS2Pjxo3Wmg0bNpCWlmZTs2vXLuLj4601K1aswGQy0aZNG2vN6tWryc3NtakJCAigXr16V/JbUO4SEhIYOXIkDRs2NDqKlNCHvx0BoG1wdVrXrXaZahERESkNNaVEREQEgBEjRvDpp5/y2Wef4enpSUJCAgkJCWRlZQFFS+JGjx7N5MmTWbJkCbt27WLw4MFUqVKFiIgIAMxmM0OHDmXMmDH8/PPPbNu2jQEDBtC8eXO6dOkCQNOmTenRoweRkZGsX7+e9evXExkZSe/evWnSpAkA3bp1IyQkhIEDB7Jt2zZ+/vlnxo4dS2RkpPUJLhEREZhMJgYPHsyuXbtYsmQJkydPJioqytDle6mpqTz00EPUrFmTgIAA3nnnHQoLC3nxxRepX78+69ev57///a9h+aTk0rLyWL6rqDE6MDzI4DQiIiIVj5bviYiI2InCcli+VxqzZ88GoFOnTjbj8+bNY/DgwQA8++yzZGVlMXz4cFJSUmjbti0rVqzA09PTWj9t2jScnZ3p168fWVlZ3HHHHcyfPx8nJydrzYIFCxg1apT1KX19+/Zl5syZ1vNOTk4sXbqU4cOH06FDB9zd3YmIiGDKlCnWGrPZTExMDCNGjCAsLIxq1aoRFRVVbLnitfbCCy+wevVqBg0axPLly3n66adZvnw52dnZ/PDDD3Ts2NHQfFJyCzfGkpFbQP2aHtwZWuvyF4iIiEipOFjs5JnEzq7aV0GkrDg7Ol2+SERKJDs79pq91zf+EWV+z7sSPivze8qlBQUFMXfuXLp06cLhw4dp2LAho0aNYvr06UZHK7H09HTMZjNpaWnWmWmVTWpmLjdP+pncgkIm3RPKQ201U0pERKSkSvq9hGZKiYiI2Am7+CmRXLWTJ08SEhICQP369XFzc2PYsGEGp5LSmvPbYXILCqlbvQoP3FTX6DgiIiIVkppSIiIidqLQ6ABSJgoLC3FxcbG+dnJywsPDw8BEUlr5BYV8ve0kAPe0DsTJ0diltSIiIhWVmlIiIiIiZchisTB48GDrUw6zs7N5/PHHizWmvvrqKyPiSQl8ueU4J1Kz8DQ580SnBkbHERERqbDUlBIREbEThQY+MU7KzqBBg2xeDxgwwKAkciUKCi3M+OVPACLa1cXNRfs0ioiIlBc1pURERETK0Lx584yOIFdh+a4ETqRm4ebiSOSt9Y2OIyIiUqGpKSUiImIntNG5iPGWbDsOQMTNQfhUNRmcRkREpGJzNDqAiIiIFCksh0MqhlmzZhEcHIybmxtt2rTht99+K9F1v//+O87OzrRq1ap8A1YQSWdzWLk/CSja4FxERETKl5pSIiIiInZs0aJFjB49mvHjx7Nt2zZuvfVWevbsSWxs7CWvS0tL4+GHH+aOO+64Rkmvf3PXHCG/0MIN/p6EBnoZHUdERKTCU1NKRETEThQ6lP0h17+pU6cydOhQhg0bRtOmTZk+fTp16tRh9uzZl7zuscceIyIigvDw8GuU9PqWkZPPgg3HAHi8YwMc9OABERGRcqemlIiIiIidys3NZcuWLXTr1s1mvFu3bqxdu/ai182bN49Dhw7x0ksvleh9cnJySE9Ptzkqm2//OMnZ7Hz8vEz0aRlgdBwREZFKQU0pERERO1GIQ5kfYqxPPvmEDh06EBAQwLFjRbNwpk+fzjfffFOi65OTkykoKMDPz89m3M/Pj4SEhAtec/DgQZ5//nkWLFiAs3PJnmkTHR2N2Wy2HnXq1CnRdRVFQaGFSUv3AjCofT2cHPW1IyIici2oKSUiImInLOVwiHFmz55NVFQUd955J6mpqRQUFADg7e3N9OnTS3Wvfy4ls1gsF1xeVlBQQEREBBMnTqRx48Ylvv+4ceNIS0uzHnFxcaXKd72L2ZPAuZx83FwceahtkNFxREREKg01pURERETKwYwZM5gzZw7jx4/HycnJOh4WFsbOnTtLdA8fHx+cnJyKzYpKTEwsNnsK4OzZs2zevJknn3wSZ2dnnJ2defnll/njjz9wdnbml19+ueD7mEwmvLy8bI7KZPbKQwDc3SoQs7uLwWlEREQqj5LN6RYREZFyp43JK5YjR47QunXrYuMmk4mMjIwS3cPV1ZU2bdoQExPDPffcYx2PiYnhrrvuKlbv5eVVrOE1a9YsfvnlF7788kuCg4NL+VFUfH/EpfLH8TQARnRuaHAaERGRykVNKREREZFyEBwczPbt2wkKsl0O9sMPPxASElLi+0RFRTFw4EDCwsIIDw/ngw8+IDY2lscffxwoWnp34sQJPv74YxwdHQkNDbW53tfXFzc3t2LjUmTe70cAqFejCnWqVzE4jYiISOWippSIiIidKDQ6gJSpZ555hhEjRpCdnY3FYmHjxo18/vnnREdH8+GHH5b4Pv379+f06dO8/PLLxMfHExoayrJly6zNrvj4eGJjY8vrw6jQ0rPzWLazaGnktP6tjA0jIiJSCTlYLBa72AfV2TXQ6AgiFYazo9Pli0SkRLKzr90/9ucFDijzew458WmZ31NKbs6cObz66qvWjcMDAwOZMGECQ4cONTjZpaWnp2M2m0lLS6vQ+0v9d80RXv5+D/V9PPh5TMcLbh4vIiIipVfS7yU0U0pERESknERGRhIZGUlycjKFhYX4+voaHUn+ZuWBJAC6hvipISUiImIAPX1PRETEThQ6lP0hxpk4cSKHDhU91c3Hx0cNKTsTdyaT3w4WNaXuD6ttcBoREZHKSU0pERERkXKwePFiGjduTLt27Zg5cyZJSUlGR5K/+WT9MSwWuKWhDw19PY2OIyIiUimpKSUiImInCsvhEOPs2LGDHTt2cPvttzN16lQCAwO58847+eyzz8jMzDQ6XqVWWGjhm+0nAOh/Ux2D04iIiFReakqJiIjYCTWlKp5mzZoxefJkDh8+zK+//kpwcDCjR4/G39/f6GiV2vc74zmVnkNVkzNdQ/yMjiMiIlJpqSklIiIicg14eHjg7u6Oq6sreXl5Rsep1N5asR8omiXl5qIn1oqIiBhFTSkRERE7YXEo+0OMdeTIESZNmkRISAhhYWFs3bqVCRMmkJCQYHS0SuvAqbMcO120fHLoLcEGpxEREancnI0OICIiIlIRhYeHs3HjRpo3b86QIUOIiIggMDDQ6FiV3qxf/wSgY+OaBHi7G5xGRESkclNTSkRExE5oD6iKpXPnznz44Yc0a9bM6Cjy/w4nneO7HfEAjLy9ocFpRERERE0pERERO6GmVMUyefJkoyPIP3yy/hgFhRbaBlcnrF51o+OIiIhUempKiYiIiJSRqKgoXnnlFTw8PIiKirpk7dSpU69RKgHILyjk0/XHABjUvp6xYURERARQU0pERMRuWIwOIFdt27Zt1ifrbdu2zeA08nc/70skr8CCydmRTk1qGh1HREREUFNKREREpMz8+uuvF/y1GG/RpjgAbm1Ukyqu+hZYRETEHjgaHUBERESKFDqU/SHGeeSRRzh79myx8YyMDB555BEDElVex05n8Mu+RACe73mDwWlERETkPDWlRERE7ERhORxinI8++oisrKxi41lZWXz88ccGJKq8Fm89AUCHhjVo6FvV4DQiIiJynuYui4iIiJSh9PR0LBYLFouFs2fP4ubmZj1XUFDAsmXL8PX1NTBh5ZJXUMiiTbEA3NemtsFpRERE5O/UlBIREbETmtlUMXh7e+Pg4ICDgwONGzcudt7BwYGJEycakKxy+mVfIqfSc/Cu4kLP0FpGxxEREZG/UVNKREREpAz9+uuvWCwWbr/9dhYvXkz16tWt51xdXQkKCiIgIMDAhJXLit2nAOjbMgA3FyeD04iIiMjfqSklIiJiJyxGB5Ay0bFjRwCOHDlC3bp1cXDQjvNGSc/O47s/TgLQp6UagSIiIvZGTSkRERE7oaflXf927NhBaGgojo6OpKWlsXPnzovWtmjR4homq5z+tymO3IJC6vt4EBZUzeg4IiIi8g9qSomIiIiUkVatWpGQkICvry+tWrXCwcEBi6X4HDgHBwcKCgoMSFi5/Lw3EYDbb/DVjDURERE75Gh0ABERESlSWA5HaaxevZo+ffoQEBCAg4MDX3/9tc35wYMHWzfwPn+0a9fOpiYnJ4eRI0fi4+ODh4cHffv25fjx4zY1KSkpDBw4ELPZjNlsZuDAgaSmptrUxMbG0qdPHzw8PPDx8WHUqFHk5uba1OzcuZOOHTvi7u5OYGAgL7/88gUbQNfSkSNHqFmzpvXXhw8f5siRI8WOw4cPG5qzMkjLymPDkdMA9LupjsFpRERE5EI0U0pEREQAyMjIoGXLlgwZMoR//etfF6zp0aMH8+bNs752dXW1OT969Gi+++47Fi5cSI0aNRgzZgy9e/dmy5YtODkVbTIdERHB8ePHWb58OQCPPvooAwcO5LvvvgOgoKCAXr16UbNmTdasWcPp06cZNGgQFouFGTNmAJCenk7Xrl3p3LkzmzZt4sCBAwwePBgPDw/GjBlT5p+bkgoKCrrgr+Xa+/3PZAotULd6FRr7eRodR0RERC5ATSkRERE7YfRG5z179qRnz56XrDGZTPj7+1/wXFpaGnPnzuWTTz6hS5cuAHz66afUqVOHn376ie7du7N3716WL1/O+vXradu2LQBz5swhPDyc/fv306RJE1asWMGePXuIi4uzPqXurbfeYvDgwUyaNAkvLy8WLFhAdnY28+fPx2QyERoayoEDB5g6dSpRUVF2sVTro48+wsfHh169egHw7LPP8sEHHxASEsLnn3+uplU5+2lP0VP3OjWpaXASERERuRgt3xMREbEThVjK/MjJySE9Pd3myMnJueKMK1euxNfXl8aNGxMZGUliYqL13JYtW8jLy6Nbt27WsYCAAEJDQ1m7di0A69atw2w2WxtSAO3atcNsNtvUhIaGWhtSAN27dycnJ4ctW7ZYazp27IjJZLKpOXnyJEePHr3ij68sTZ48GXd3d6Ao78yZM3njjTfw8fHh6aefNjhdxZaZm8/3O+MB6Kun7omIiNgtNaVEREQqsOjoaOveTeeP6OjoK7pXz549WbBgAb/88gtvvfUWmzZt4vbbb7c2uRISEnB1daVaNdunnPn5+ZGQkGCt8fX1LXZvX19fmxo/Pz+b89WqVcPV1fWSNedfn68xWlxcHA0bNgTg66+/5r777uPRRx8lOjqa3377zeB0Fdvmoynk5hfi62mijZ66JyIiYre0fE9ERMROlHZj8pIYN24cUVFRNmN/n11UGv3797f+OjQ0lLCwMIKCgli6dCn33nvvRa+zWCw2y+kutLSuLGrOb3JuD0v3AKpWrcrp06epW7cuK1assM6OcnNzIysry+B0Fdsv+4pm8HVo6GM3fx5ERESkODWlREREKjCTyXTFTajLqVWrFkFBQRw8eBAAf39/cnNzSUlJsZktlZiYSPv27a01p06dKnavpKQk60wnf39/NmzYYHM+JSWFvLw8m5p/zog6v5TwnzOojNK1a1eGDRtG69atOXDggHVvqd27d1OvXj1jw1VgufmFfL+jaOme9pMSERGxb1q+JyIiYics5XCUp9OnTxMXF0etWrUAaNOmDS4uLsTExFhr4uPj2bVrl7UpFR4eTlpaGhs3brTWbNiwgbS0NJuaXbt2ER8fb61ZsWIFJpOJNm3aWGtWr15Nbm6uTU1AQIDdNHzeffddwsPDSUpKYvHixdSoUQMo2nvrwQcfNDhdxbVyfyLJ53JwcnSga4h9NChFRETkwjRTSkRExE6Ux/K90jh37hx//vmn9fWRI0fYvn071atXp3r16kyYMIF//etf1KpVi6NHj/LCCy/g4+PDPffcA4DZbGbo0KGMGTOGGjVqUL16dcaOHUvz5s2tT+Nr2rQpPXr0IDIykvfffx+ARx99lN69e9OkSRMAunXrRkhICAMHDuTNN9/kzJkzjB07lsjISLy8vACIiIhg4sSJDB48mBdeeIGDBw8yefJkXnzxRbtZruXt7c3MmTOLjU+cONGANJXHj7uLZuINaFuXKq76VldERMSe6W9qERERAWDz5s107tzZ+vr8XlSDBg1i9uzZ7Ny5k48//pjU1FRq1apF586dWbRoEZ6entZrpk2bhrOzM/369SMrK4s77riD+fPn4+TkZK1ZsGABo0aNsj6lr2/fvjbNGycnJ5YuXcrw4cPp0KED7u7uREREMGXKFGuN2WwmJiaGESNGEBYWRrVq1YiKiiq2f5bRUlNTmTt3Lnv37sXBwYGmTZsydOhQzGaz0dEqpMJCC6sOFC3j7Brib3AaERERuRwHy/ldQQ3m7BpodASRCsPZ0enyRSJSItnZsdfsvV6s91CZ3/PlowvK/J5SMps3b6Z79+64u7tz8803Y7FY2Lx5M1lZWaxYsYIbb7zR6IgXlZ6ejtlsJi0tzTo77XrwZ+JZukxdjZuLIzte6o6rs3aqEBERMUJJv5fQTCkRERGRcvD000/Tt29f5syZg7Nz0bdc+fn5DBs2jNGjR7N69WqDE1Y8K/cnAdAi0FsNKRERkeuAmlIiIiJ2orDctyaXa2nz5s02DSkAZ2dnnn32WcLCwgxMVjFZLBYWbYoDoHfLWganERERkZLQj5BERETsxPX29D25NC8vL2Jjiy//jIuLs9mHS8rGgVPnOJh4DmdHB+5qqW0hRERErgdqSomIiIiUg/79+zN06FAWLVpEXFwcx48fZ+HChQwbNowHH3zQ6HgVzoYjpwEI9vHAXMXF4DQiIiJSElq+JyIiYicKjQ4gZWrKlCk4ODjw8MMPk5+fD4CLiwtPPPEEr732msHpKp4fdiYAcFvjmgYnERERkZJSU0pERESkHLi6uvL2228THR3NoUOHsFgsNGzYkCpVqhgdrcKJPZ3JusNFM6UGhdczNoyIiIiUmJbviYiI2IlCLGV+yLWXmZnJiBEjCAwMxNfXl2HDhlGrVi1atGihhlQ5+faPEwB0aFiDujX0ORYREbleqCklIiJiJ7TRecXw0ksvMX/+fHr16sUDDzxATEwMTzzxhNGxKrTlu4uW7vVtGWBwEhERESkNLd8TERERKUNfffUVc+fO5YEHHgBgwIABdOjQgYKCApycnAxOV/Gcychlz8l0ADo29jU4jYiIiJSGZkqJiIjYicJyOOTai4uL49Zbb7W+vvnmm3F2dubkyZMGpqq4Vh1IpNACTWt54W92MzqOiIiIlIKaUiIiIiJlqKCgAFdXV5sxZ2dn6xP4pGxtOZYCQPsGNQxOIiIiIqWl5XsiIiJ2QhuTVwwWi4XBgwdjMpmsY9nZ2Tz++ON4eHhYx7766isj4lU4v/9Z9NS9m+pVNziJiIiIlJaaUiIiInZCLamKYdCgQcXGBgwYYECSii89O48jyRkA3BjkbWwYERERKTU1pURERETK0Lx584yOUGmcX7pXp7o7vp7aT0pEROR6o6aUiIiIndDG5CKl8/0f8QDcFKSleyIiItcjbXQuIiIiItelPfHpADTwrWpwEhEREbkSV9SU+uSTT+jQoQMBAQEcO3YMgOnTp/PNN9+UaTgREZHKxFIO/xOpqHLyCziUeA6AbiF+BqcRERGRK1HqptTs2bOJiorizjvvJDU1lYKCAgC8vb2ZPn16WecTERGpNArL4RCpqHafTCe3oOhPeUPNlBIREbkulbopNWPGDObMmcP48eNxcnKyjoeFhbFz584yDSciIiIiciFrDiYDRbOkHBwcDE4jIiIiV6LUTakjR47QunXrYuMmk4mMjIwyCSUiIlIZFWIp80OMpS0Pys8PuxIA6NJUS/dERESuV6VuSgUHB7N9+/Zi4z/88AMhISFlkUlERETkuqctD8pPWmYee/9/k/NON9Q0OI2IiIhcqVI3pZ555hlGjBjBokWLsFgsbNy4kUmTJvHCCy/wzDPPlEdGERGRSsFSDocYR1selJ+1h4qW7tWv6YGvp5vBaURERORKOZf2giFDhpCfn8+zzz5LZmYmERERBAYG8vbbb/PAAw+UR0YREZFKQcvtKhZteVB+Vuw5BcCtDX0MTiIiIiJXo9QzpQAiIyM5duwYiYmJJCQkEBcXx9ChQ8s6m1zGY48+zNYtMZxJ3seZ5H2sWf0tPbp3vmh9h/Y3sXrl15yK38XZtD/ZtXMVT42KLPecoaE38MtPX3I27U+OHdnMv8ePtotcIv90yy03s3jxfzl8eBPZ2bH06dPtkvX+/r589NE77NjxK5mZR3nzzZeuSc5mzZoQE/M/UlIOcOjQRl544amL1oaHh3Hu3GE2bPjhmmQTkb9oy4PyYbFYWH0gCYCezWsZnEZERESuRqlnSv2dj49+OmWkEyfiGT8+mj8PHQXg4YH389Xi/xJ2c3f27DlQrD4jM5N3Z89j5869ZGRk0qHDzcx+93UyMjL5cO6CK8oQFFSbQwc34OwaeMHznp5VWb7sc1auWku79r1o1Kg+//1wGhkZWUyb/n655RK5ElWqVGHnzj18/PH/WLTog8vWm0yuJCWd4fXXZzJyZNk05oOCarN//1rc3Ope8LynZ1WWLl3A6tXr6NChN40a1WfOnLfIyMjk7bfn2NR6eXkyd+40fv31d3x99d/r60Gh0QGkTJ3f8iA7O9u65cHnn39OdHQ0H374odHxrlvHTmdyOiMXFycHWtf1NjqOiIiIXIVSN6WCg4Mv+djdw4cPX1UgKbnvl8bYvP7Pi6/z2KMDaXvzjRdsSm3fvpvt23dbXx87dpx77u7JLbe0tWn+DHq4H2PHDie4Xh2OHjvOzJn/5b33P7qijBEP3oubm4lHhj5Nbm4uu3fvp3Gj+ox+KtLalCppLpHytmLFSlasWFni+mPHjjN27AQABg3qd9G6hx++n6iox6lXrw7Hjh3n3Xfn8cEHn1xRxgceuBs3NxPDho0hNzeXPXsO0KhRfUaNiizWlJo5M5pFi76moKDwsrO+RKTsacuD8vHbwaJZUq3qeGNydrpMtYiIiNizUjelRo8ebfM6Ly+Pbdu2sXz5cm10biBHR0fuu683Hh5VWL9hS4muadWqGeHtwnjxpTesY0MfieClF8cwavS/2b59F61ahfL+7DfJyMzkk0++KHWudu3asPq39eTm5lrHVsSsZPKkF6hXrw5Hj8aVKJfI9eqRRx7k3/+O4umn/8Mff+ymZctmzJr1OpmZWXz66Zelvl+7dm347bcNNl9TMTGrePXV522+ph5++H7q1w9iyJCnGDduVJl9PFK+LNpTqsKJjIwkMjKS5ORkCgsL8fX1NTrSdW/zsRQA2jfQDFAREZHrXambUk89deG9S9599102b9581YGkdEJDb2DN6m9xczNx7lwG990/jL17D17ymqOHN1OzZnWcnZ15+ZWp/Hfe59Zz418YzTPPvczXXxftP3P0aBwhTRvz6LABV9SU8verydFjto2nU6eS//+cr01T6lK5RK5X48aN4vnnX+Gbb5YDRV9TTZs2YujQiCtqSvn51eTYseM2Y4mJydZzR4/G0aBBPV555XnuuOM+6yPo5fqg5XsVl7Y8KDt/xKUC0EpL90RERK57V7Wn1N/17NmTcePGMW/evMvW5uTkkJOTYzNmsVguuSxQLmz//kO0uakb3mYv7r33Tv47dzq3d/nXJRtTnW6/h6pVPWh7841MnvQCfx46wqJF3+DjU526dQOZ8/5bvD/7TWu9s7MTaWlnra//2P4LQXVrA1h/z1LP/LVc8FjscVq2ut362vKPH/yfv8byjxMXyyVyvfLxqU6dOoG8996bzJr1unX8n19TW7f+RN26Rfuynf/6SE7eaz0fG3uCG2/sYn39z6+d8//ptFgsODo68tFHM3jllWn8+eeRMv+YRKTktOVB2UvLzOPo6UwAWtX2NjaMiIiIXLUya0p9+eWXVK9evUS10dHRTJw40WbMwbEqDk5eZRWn0sjLy+PQ/290vmXrDsLatGLkk8MYPuK5i15zfnbSrl378POryYv/GcOiRd/g6Fj0MMbHnniGjRu32Vzz99kWffoOxMXFBYDAAH9++XkxbW76a7+avLw8668TTiXh71/T5l6+vjUAOJWYVKJcIter819Tw4c/V+xrqrDwrzkxd989CGfnov8cBwb6ExPzBTff3MN6Pj8/3/rrU6eS8POz/ZqqWbNoBkZiYjKenlUJC2tJq1bNmD79ZWsOR0dHzp07TO/eA1i5cm0ZfpRSlrR8r2LRlgdlb0vsGaCoGV/Nw9XgNCIiInK1St2Uat26tc1P/SwWCwkJCSQlJTFr1qwS3WPcuHFERUXZjFWrcUNpo8gFODg4YDKV/Js0BwcHTK5F9YmJyRw/Hk/94CA+/3zJRa+JjT1h/fX5fyyfb4z90/r1W3j1ledwcXGxNqu6dunIiRPxF9xP6kK5RK5X57+mgoPrsnDh1xet+/vX1PkG8OHDxy5Yu379Fl5+2fZrqkuX2zhxIoGjR+NwcHCwmVUF8NhjD9OpU3sefPDxS37diUjZ0pYHZW/PyXQA+rQIMDiJiIiIlIVSN6Xuvvtum9eOjo7UrFmTTp06ccMNJWssmUwmTCaTzZiW7pXeq688z/LlvxB3/CSenlXp3+8uOnYMp1fvhwCY9OrzBATUYsgjRd8UP/H4IOLiTrJv/58AdGh/E1FPP8a7s/5acvnyK28xfdorpKefZfmPv2IyudLmxhZUq+bN9Lc/KHXGzxcu4T//fpr/zp3Ga6/PoGHDYJ5/biSvTppurSlJLpFrwcOjCg0a1LO+rlevDi1ahJCSkkpc3EleeeU5AgL8GTr0aWtNixYh/3+tBzVrVqdFixByc/PYt69oCe2kSdN4662JpKef48fzX1NtWuDtbeadd0r/SPhFi75h/PjRfPjhW7z++kwaNgzm2WdHMHny20DRDwr++fTNpKTTZGfnXPCpnGJftKdU5VCaLQ/E1p74oqZUswDNrhcREakIStWUys/Pp169enTv3h1/f//yyiQl5Ovrw/x571Crli9paWfZuXMvvXo/xE8//waAv78fdev89ZNER0dHXn31eYLr1SU/P59Dh4/xwvhoPpjz16Pp/zvvczKzshgT9QSvRY8nIyOTXbv28faM0v/jGSA9/Sw97nyQGW9PYsO6ZaSkpDH97Q+YNv39UuUSuRbatGnBihX/s75+882XAPjkky+IjByDv78vderY/nR+48blNtc/8MA9HDsWR5MmHQCYN28hmZlZPP30Y0yePI6MjCx2797HjBlzryhjevpZevV6iLfffpW1a78nJSWdd975kLffnnNF9xP7UvjPTfikQirNlgdia19C0X58DX2rGpxEREREyoKD5Z875l5GlSpV2Lt3L0FBQWUaxNk1sEzvJ1KZOTs6GR1BpMLIzo69Zu81MOjeMr/nJ8e+KvN7SslcbsuDRx991MB0l5aeno7ZbCYtLQ0vL/uYlXQ2O4/mE1YAsGl8F2p6mi5zhYiIiBilpN9LlHr5Xtu2bdm2bVuZN6VEREQqO82TqljKYsuD82bNmsWbb75JfHw8zZo1Y/r06dx6660XrP3qq6+YPXs227dvJycnh2bNmjFhwgS6d+9+pR+KXfgjLg2AALObGlIiIiIVRKmbUsOHD2fMmDEcP36cNm3a4OHhYXO+RYsWZRZORERE5HpUllseLFq0iNGjRzNr1iw6dOjA+++/T8+ePdmzZw9169YtVr969Wq6du3K5MmT8fb2Zt68efTp04cNGzbQunXrq8pipD+OpwLQpp6WPoqIiFQUJV6+98gjjzB9+nS8vb2L38TBAYvFgoODg/XJUaWl5XsiZUfL90TKzrVcvhcRdE+Z3/OzYxd/mqqUr7La8qBt27bceOONzJ492zrWtGlT7r77bqKjo0t0j2bNmtG/f39efPHFEtXb4/K9pxZu45vtJ3mmexNGdG5odBwRERG5hJJ+L+FY0ht+9NFHZGdnc+TIkWLH4cOHrf8vIiIiV8ZSDv8rjdWrV9OnTx8CAgJwcHDg66+/ts1nsTBhwgQCAv6vvTuPiuJK2wD+NEs3zdYiyCYIqCAajVGYKPoZ4ijiEnV04jIYFEeNRg2jxhiXSXA3EyNxTNwmByU6Cmqi0cQkaowbcUOEuOC4org0UZRNduj7/cFQY7MoIL0Iz29On5OuulX3rappun3rvbdcoVQq8frrr+PixYtabQoLC/Huu+/CwcEBVlZWGDRoEO7cuaPVJiMjA6GhoVCpVFCpVAgNDUVmZqZWm9TUVAwcOBBWVlZwcHBAeHg4ioqKtNqcP38egYGBUCqVaN68ORYuXIhaTpWpU+VTHjyPoqIiJCQkoE+fPlrL+/Tpg+PHj9doHxqNBjk5OS/85Or/UZdNct7GycbAkRAREVF9qfHwvfIfeZxLioiIqGHKzc1Fx44dMXbsWPz5z3+utP6TTz5BZGQkoqOj4ePjg8WLFyMoKAiXL1+GjU1ZomDatGn47rvvEBsbC3t7e7z33nt44403kJCQAFPTsirOkJAQ3LlzBz/9VPb0yrfffhuhoaH47rvvAAClpaUYMGAAmjVrhri4ODx8+BBjxoyBEAKff/45gLK7b0FBQejZsyfi4+Nx5coVhIWFwcrKCu+9954+Ttcz1ceUB+np6SgtLYWTk5PWcicnJ6SlpdUojhUrViA3NxfDhw+vtk1hYSEKCwul99nZ2TXat74UFJfi+oPHAIC2rsZRuUVERETPr1ZzSj35BBkiIiKqXxoD99+vXz/069evynVCCKxcuRLz5s3D0KFlTwn86quv4OTkhK1bt2LixInIyspCVFQUNm/ejN69ewMA/v3vf8Pd3R0///wzgoODcenSJfz00084efIkunTpAgD48ssvERAQgMuXL6NNmzbYv38/kpOTcfv2bbi6ugIoS6yEhYVhyZIlsLW1xZYtW1BQUIDo6GgoFAq0b98eV65cQWRkJGbMmGHQ3yzlUx6MGDECABAeHi6tq+uUBxWPp3wfzxITE4P58+dj9+7dcHR0rLbdsmXLsGDBghrHo2/J6myUaATsreRwVVkYOhwiIiKqJzUevgcAPj4+aNq06VNfRERE1PCkpKQgLS1NaxiZQqFAYGCgNIwsISEBxcXFWm1cXV3Rvn17qc2JEyegUqmkhBQAdO3aFSqVSqtN+/btpYQUAAQHB6OwsBAJCQlSm8DAQCgUCq029+7dw82bN+v/BNRCfU554ODgAFNT00pVUffv369UPVXRtm3bMG7cOGzfvl1KElZnzpw5yMrKkl63b9+uUXz6cvFu2ZP3Xmqu4k1SIiKiBqRWlVILFiyASqXSVSxERESNmqaWc0DVRMVhWUBZMunJZE5NlCdFqhpGduvWLamNXC6HnZ1dpTbl26elpVVZsePo6KjVpmI/dnZ2kMvlWm08PT0r9VO+zsvLq1bHV5/qc8oDuVwOPz8/HDhwAEOG/G8i/AMHDmDw4MHVbhcTE4O//vWviImJwYABA57ZT13+P6FPl9LK5pN6iUP3iIiIGpRaJaVGjhz51NJvIiIiqrvaTkxeE1UNy4qIiMD8+fPrtL+6DCOr2Kaq9vXRpjwZZAyVNPUZw4wZMxAaGgp/f38EBATgX//6F1JTUzFp0iQAZVVOd+/exaZNmwCUJaRGjx6Nf/7zn+jatauUyFMqlS/szcXfbmcCYFKKiIiooalxUsoYfuARERFR7cyZMwczZszQWlaXihhnZ2cAZVVILi4u0vInh5E5OzujqKgIGRkZWtVS9+/fR7du3aQ2v//+e6X9P3jwQGs/p06d0lqfkZGB4uJirTZVDWkDKldzGYKPj88zfzs9evSoRvsaMWIEHj58iIULF0KtVqN9+/b44YcfpEostVqN1NRUqf369etRUlKCKVOmYMqUKdLyMWPGIDo6uvYHY2DFpRr857+VUi83b2LYYIiIiKhe1frpe0RERKQbupjovL6GZXl5ecHZ2RkHDhxAp06dAABFRUU4cuQI/vGPfwAA/Pz8YG5ujgMHDkhPelOr1bhw4QI++eQTAEBAQACysrJw+vRpvPrqqwCAU6dOISsrS0pcBQQEYMmSJVCr1VICbP/+/VAoFPDz85PazJ07F0VFRZDL5VIbV1fXSsP6DKG+pzyYPHkyJk+eXOW6iommw4cP11u/xiAlPRelGgEruSnc7JSGDoeIiIjqUY2TUhqNoZ8JRERERLr0+PFjXLt2TXqfkpKCpKQkNG3aFC1atMC0adOwdOlSeHt7w9vbG0uXLoWlpSVCQkIAACqVCuPGjcN7770He3t7NG3aFDNnzkSHDh2kibbbtm2Lvn37YsKECVi/fj0A4O2338Ybb7yBNm3aAAD69OmDdu3aITQ0FMuXL8ejR48wc+ZMTJgwAba2ZcO3QkJCsGDBAoSFhWHu3Lm4evUqli5dio8++sgoqrs55UH9ufEgFwBQohEwMTH8tSUiIqL6U6s5pYiIiEh3DF2VfObMGfTs2VN6Xz7sr3zY16xZs5Cfn4/JkycjIyMDXbp0wf79+2FjYyNt89lnn8HMzAzDhw9Hfn4+evXqhejoaJiamkpttmzZgvDwcOkpfYMGDcIXX3whrTc1NcXevXsxefJkdO/eHUqlEiEhIfj000+lNiqVCgcOHMCUKVPg7+8POzs7zJgxo9JQRUMwhqRYQ3LtftnQvf4dXJ7RkoiIiF40MmHoX8D/ZSZvbugQiBoMMxPTZzciohopKEh9dqN6MrjFG/W+z92p39f7PunpTExMqn3K4IsiOzsbKpUKWVlZUnWaoUzZchZ7z6vxQV9fvPN6K4PGQkRERDVT098SrJQiIiIiqkec8qB+XbyXBQBoxyfvERERNThMShERERkJpjKItOUXleLmwzwAQDsXJqWIiIgaGhNDB0BEREREVJVkdTYAQG5qAgdruYGjISIiovrGSikiIiIjIWAU0zwSGY3ySc5bNrPiBPJEREQNEJNSRERERkLDpBSRltRHZUP3/uDZ1MCREBERkS5w+B4RERERGaXbj/IBAC5NLAwcCREREekCK6WIiIiMhBCslCJ60pXfy4bvtXGyMXAkREREpAuslCIiIiIio1NcqsF/0sqSUj5MShERETVIrJQiIiIyEhpDB0BkRK4/eAwAsDA3QfMmSgNHQ0RERLrApBQREZGR4NP3iP4nMTUTAGBuagITEz55j4iIqCHi8D0iIiIiMjqJqRkAgNEBHgaOhIiIiHSFlVJERERGQsNKKSLJjQe5AIC2LrYGjoSIiIh0hZVSRERERGR0bj7MAwC0aGpp4EiIiIhIV1gpRUREZCSEYKUUEQDkFBQj/XEhAMDTwcrA0RAREZGuMClFRERkJDh8j6jMld9zAADOthawtTA3cDRERESkKxy+R0RERERGpXw+qdaO1gaOhIiIiHSJlVJERERGQrBSiggAkProv/NJ2XM+KSIiooaMlVJEREREZFRS0ssqpbzsOZ8UERFRQ8ZKKSIiIiOh4UTnRACA2xn5AAD3pkoDR0JERES6xKQUERGRkWBKiqjMrYdllVItmrJSioiIqCHj8D0iIiIiMhpZecXIzCsGAHhwTikiIqIGjZVSRERERkLDWiki3Eh/DABwsFbASsGfqkRERA0ZK6WIiIiIyGhIk5w7sEqKiIiooePtJyIiIiPBSiki4Nr9skqp1o7WBo6EiIiIdI1JKSIiIiMh+PQ9Ilz9b1KqjZONgSMhIiIiXePwPSIiIiIyGjcelCWlWjZjpRQREVFDx0opIiIiI8Hhe9TYFZVopDmlfFgpRURE1OCxUoqIiIiIjMKN9MfQCMBaYQYnW4WhwyEiIiIdY6UUERGRkRCslKJG7vfsQgDA48ISyGQyA0dDREREusakFBERkZHgROfU2P2eXQAA6OHtYOBIiIiISB84fI+IiIiIjMLvWWVJKWdbCwNHQkRERPrASikiIiIjwYnOqbG7nZEHAHBvamngSIiIiEgfWClFREREREbhTkY+AMC1idLAkRAREZE+sFKKiIjISHBOKWrsbj0sq5TycmClFBERUWPApBQREZGR4PA9asw0GiFNdM5KKSIiosaBw/eIiIiIyODScwtRohGQyQAHa4WhwyEiIiI9YKUUERGRkRCslKJGLPW/Q/dcVUqYm/K+KRERUWPAb3wiIiIiMrjy+aRa8Ml7REREjQaTUkREREZCI0S9v2pj/vz5kMlkWi9nZ2dpvRAC8+fPh6urK5RKJV5//XVcvHhRax+FhYV499134eDgACsrKwwaNAh37tzRapORkYHQ0FCoVCqoVCqEhoYiMzNTq01qaioGDhwIKysrODg4IDw8HEVFRbU7ofRCuZ3BpBQREVFjw6QUERGRkRA6+F9tvfTSS1Cr1dLr/Pnz0rpPPvkEkZGR+OKLLxAfHw9nZ2cEBQUhJydHajNt2jTs2rULsbGxiIuLw+PHj/HGG2+gtLRUahMSEoKkpCT89NNP+Omnn5CUlITQ0FBpfWlpKQYMGIDc3FzExcUhNjYW33zzDd577706nll6EdzJyAcAuDflJOdERESNBeeUIiIiIomZmZlWdVQ5IQRWrlyJefPmYejQoQCAr776Ck5OTti6dSsmTpyIrKwsREVFYfPmzejduzcA4N///jfc3d3x888/Izg4GJcuXcJPP/2EkydPokuXLgCAL7/8EgEBAbh8+TLatGmD/fv3Izk5Gbdv34arqysAYMWKFQgLC8OSJUtga2urp7NB+qTOKktKuaiYlCIiImosWClFRERkJHQxfK+wsBDZ2dlar8LCwmpjuHr1KlxdXeHl5YWRI0fixo0bAICUlBSkpaWhT58+UluFQoHAwEAcP34cAJCQkIDi4mKtNq6urmjfvr3U5sSJE1CpVFJCCgC6du0KlUql1aZ9+/ZSQgoAgoODUVhYiISEhHo402SM0nPKhmc2s+GT94iIiBoLJqWIiIgasGXLlklzN5W/li1bVmXbLl26YNOmTdi3bx++/PJLpKWloVu3bnj48CHS0tIAAE5OTlrbODk5SevS0tIgl8thZ2f31DaOjo6V+nZ0dNRqU7EfOzs7yOVyqQ01PL/nFAAAHG2ZlCIiImosOHyPiIjISNRlDqhnmTNnDmbMmKG1TKGo+h/9/fr1k/67Q4cOCAgIQKtWrfDVV1+ha9euAACZTKa1jRCi0rKKKrapqn1d2lDDUVBcisy8YgCAiy2H7xERETUWrJQiIiIyEroYvqdQKGBra6v1qi4pVZGVlRU6dOiAq1evSvNMVaxUun//vlTV5OzsjKKiImRkZDy1ze+//16prwcPHmi1qdhPRkYGiouLK1VQUcNwN7NsPikruSlslbxnSkRE1FgwKUVERERVKiwsxKVLl+Di4gIvLy84OzvjwIED0vqioiIcOXIE3bp1AwD4+fnB3Nxcq41arcaFCxekNgEBAcjKysLp06elNqdOnUJWVpZWmwsXLkCtVktt9u/fD4VCAT8/P50eMxnG9fuPAQAe9lashiMiImpEeCuKiIjISOhi+F5tzJw5EwMHDkSLFi1w//59LF68GNnZ2RgzZgxkMhmmTZuGpUuXwtvbG97e3li6dCksLS0REhICAFCpVBg3bhzee+892Nvbo2nTppg5cyY6dOggPY2vbdu26Nu3LyZMmID169cDAN5++2288cYbaNOmDQCgT58+aNeuHUJDQ7F8+XI8evQIM2fOxIQJE/jkvQYqJT0XANDK0drAkRAREZE+MSlFREREAIA7d+7gL3/5C9LT09GsWTN07doVJ0+ehIeHBwBg1qxZyM/Px+TJk5GRkYEuXbpg//79sLGxkfbx2WefwczMDMOHD0d+fj569eqF6OhomJqaSm22bNmC8PBw6Sl9gwYNwhdffCGtNzU1xd69ezF58mR0794dSqUSISEh+PTTT/V0Jkjf1Fllk5w3b8L5pIiIiBoTmRDCsLdl/8tM3tzQIRA1GGYmps9uREQ1UlCQqre+Wjl0rvd9Xk8/W+/7pIYvOzsbKpUKWVlZeqlOG7H+BE6lPMLyN1/GMH93nfdHREREulXT3xKslCIiIjIShh6+R2QoqY/yAADeTjbPaElEREQNCSc6JyIiIiKD0WgEHuQUAgCcbGv2ZEgiIiJqGFgpRUREZCSE0Bg6BCK9S39ciBKNgIkMsLdiUoqIiKgxYaUUERERERlM+dA9F5UScjP+NCUiImpMWClFRERkJDScU4oaobuZ+QAANzs+eY+IiKixYVKKiIjISBjJA3GJ9Cr1YVmlVHMmpYiIiBodJqWIiIiIyGBSHuYCAFo1szZwJEREVRNCoKSkBKWlpYYOhchomJqawszMDDKZ7Ln2w6QUERGRkeDwPWqMbv93TikO3yMiY1RUVAS1Wo28vDxDh0JkdCwtLeHi4gK5XF7nfTApRUREREQGcy+zAADgZmdp4EiIiLRpNBqkpKTA1NQUrq6ukMvlz10VQtQQCCFQVFSEBw8eICUlBd7e3jAxqdvDSpiUIiIiMhKcU4oam4LiUtzLKpvovEVTJqWIyLgUFRVBo9HA3d0dlpb8G0X0JKVSCXNzc9y6dQtFRUWwsLCo036YlCIiIjISGialqJG58SAXQgAqpTkcrOte+k9EpEt1rQAhaujq47PBTxcRERERGUTqo7JJzj0drDgkhoiIqBFiUoqIiMhICB38j8iYJatzAACtHKwMHAkRUeMyf/58vPLKK/W6z8OHD0MmkyEzMxMAEB0djSZNmtRrH9W5fPkynJ2dkZOTo5f+GoPvv/8enTp1gkaj0Wk/TEoRERERkUEkpmYAAF5p0cSwgRARNTBhYWGQyWSQyWQwNzdHy5YtMXPmTOTmllWozpw5EwcPHtRpDCNGjMCVK1d02ke5efPmYcqUKbCxsam0rk2bNpDL5bh7926ldZ6enli5cmWl5StXroSnp6fWsuzsbMybNw++vr6wsLCAs7MzevfujZ07d+p0XtDz588jMDAQSqUSzZs3x8KFC5/aX3lysKpXfHy81O7gwYPo1q0bbGxs4OLigg8++AAlJSXS+jfeeAMymQxbt27V2bEBTEoREREZDSFEvb+IjFWpRiApNRMA0MndzrDBEBE1QH379oVarcaNGzewePFirFmzBjNnzgQAWFtbw97eXqf9K5VKODo66rQPALhz5w727NmDsWPHVloXFxeHgoICDBs2DNHR0XXuIzMzE926dcOmTZswZ84cnD17FkePHsWIESMwa9YsZGVlPccRVC87OxtBQUFwdXVFfHw8Pv/8c3z66aeIjIysdptu3bpBrVZrvcaPHw9PT0/4+/sDAM6dO4f+/fujb9++SExMRGxsLPbs2YPZs2dr7Wvs2LH4/PPPdXJs5ZiUIiIiMhIaiHp/UcOwZs0aeHl5wcLCAn5+fjh27NhT2x85cgR+fn6wsLBAy5YtsW7dOj1FWnOX1NnIKSyB0twU7VxtDR0OEVGDo1Ao4OzsDHd3d4SEhGDUqFH49ttvAVQevhcWFoY//elPWLBgARwdHWFra4uJEyeiqKhIaiOEwCeffIKWLVtCqVSiY8eO+Prrr6vtv+LwvfI+N2/eDE9PT6hUKowcOVJryF1t+wCA7du3o2PHjnBzc6u0LioqCiEhIQgNDcWGDRvqfMNu7ty5uHnzJk6dOoUxY8agXbt28PHxwYQJE5CUlARra+s67fdZtmzZgoKCAkRHR6N9+/YYOnQo5s6di8jIyGqPRS6Xw9nZWXrZ29tjz549+Otf/yrN3xgbG4uXX34ZH330EVq3bo3AwEAsW7YMq1ev1roegwYNwunTp3Hjxg2dHB/ApBQRERGRUdu2bRumTZuGefPmITExET169EC/fv2QmppaZfuUlBT0798fPXr0QGJiIubOnYvw8HB88803eo786Y5ceQAAeNWrKUxNOMk5Eb0YhBDIKyoxyOt5K6CVSiWKi4urXX/w4EFcunQJhw4dQkxMDHbt2oUFCxZI6//+979j48aNWLt2LS5evIjp06fjrbfewpEjR2ocw/Xr1/Htt9/i+++/x/fff48jR47g448/fq4+jh49KlUAPSknJwc7duzAW2+9haCgIOTm5uLw4cM1jrWcRqNBbGwsRo0aBVdX10rrra2tYWZmVuW2x44dg7W19VNfS5curbbvEydOIDAwEAqFQloWHByMe/fu4ebNmzWKf8+ePUhPT0dYWJi0rLCwEBYWFlrtlEolCgoKkJCQIC3z8PCAo6PjM2+GPY+qzxwRERHpHYfbUVUiIyMxbtw4jB8/HkDZPBf79u3D2rVrsWzZskrt161bhxYtWkhzZLRt2xZnzpzBp59+ij//+c/6DP2p9p5TAwD+6Kv7oR1ERPUlv7gU7T7aZ5C+kxcGw1Jet3/Cnz59Glu3bkWvXr2qbSOXy7FhwwZYWlripZdewsKFC/H+++9j0aJFyM/PR2RkJH755RcEBAQAAFq2bIm4uDisX78egYGBNYpDo9EgOjpamvspNDQUBw8exJIlS5Cbm1unPm7evAk/P79Ky2NjY+Ht7Y2XXnoJADBy5EhERUWhZ8+eNYq1XHp6OjIyMuDr61ur7QDA398fSUlJT23TtGnTatelpaVVmtvKyclJWufl5fXMGKKiohAcHAx3d3dpWXBwMFauXImYmBgMHz4caWlpWLx4MQBArVZrbd+8efMaJ8DqgkkpIiIiIiNVVFSEhISESnM89OnTB8ePH69ymxMnTqBPnz5ay4KDgxEVFYXi4mKYm5tX2qawsBCFhYXS++zs7HqIvnqHL99HsjobpiYyDHjZRad9ERE1Vt9//z2sra1RUlKC4uJiDB48+KnzA3Xs2BGWlpbS+4CAADx+/Bi3b9/G/fv3UVBQgKCgIK1tioqK0KlTpxrH5OnpqTUZuYuLC+7fvw8ASE5OrlMf+fn5lap+gLJkzFtvvSW9f+utt/Daa68hMzOzVk8FLL9pWD70rTaUSiVat25d6+2eVLHf2sRz584d7Nu3D9u3b9da3qdPHyxfvhyTJk1CaGgoFAoFPvzwQ8TFxcHU1LTSMeTl5T3XMTwNk1JERERGQsNKKaogPT0dpaWl0l3Rck5OTkhLS6tym7S0tCrbl5SUID09HS4ulZNAy5Yt0xqioWtHr6QDAHr5OsLBWvGM1kRExkNpborkhcEG67s2evbsibVr18Lc3Byurq5V3pSoCZlMBo1GAwDYu3cvmjdvrrX+yaFlz1Ixhif3Xdc+HBwckJGRobUsOTkZp06dQnx8PD744ANpeWlpKWJiYvDOO+8AAGxtbaucpDwzMxMqlQoA0KxZM9jZ2eHSpUs1PUzJsWPH0K9fv6e2mTt3LubOnVvlOmdn50rf9+VJvIrf9VXZuHEj7O3tMWjQoErrZsyYgenTp0OtVsPOzg43b97EnDlzKlVfPXr0CM2aNXtmX3XFpBQREZGR4PA9qk5Vd0mfdoe0tndV58yZgxkzZkjvs7Oztcr869usvm1gZirDlNef7+4xEZG+yWSyOg+h0zcrK6taVen89ttvyM/Ph1KpBACcPHkS1tbWcHNzg52dHRQKBVJTU2s8VK+22rVrV6c+OnXqhOTkZK1lUVFReO2117B69Wqt5Zs3b0ZUVJSUlPL19UV8fHylfcbHx6NNmzYAABMTE4wYMQKbN29GREREpXmlcnNzoVAoqpxX6nmH7wUEBGDu3LkoKiqCXC4HAOzfvx+urq6VhvVVJITAxo0bMXr06GoTkjKZTDqemJgYuLu7o3PnztL6goICXL9+vVbVcLX1YnyaiIiIiBohBwcHmJqaVnmXtLo7pNXdVTUzM6v28d8KhaJWd7qfl4W5Keb2b6u3/oiI6NmKioowbtw4/P3vf8etW7cQERGBqVOnwsTEBDY2Npg5cyamT58OjUaD//u//0N2djaOHz8Oa2trjBkz5rn7r2sfwcHBGD9+PEpLS2Fqaori4mJs3rwZCxcuRPv27bXajh8/Hp988gl+++03dOzYETNmzED37t2xcOFCvPnmmwCAb775Bj/99JPWMPmlS5fi8OHD6NKlC5YsWQJ/f3+Ym5vj2LFjWLZsGeLj46scEvi8w/dCQkKwYMEChIWFYe7cubh69SqWLl2Kjz76SLrRdPr0aYwePRoHDx7UqjD75ZdfkJKSgnHjxlW57+XLl6Nv374wMTHBzp078fHHH2P79u1aw/dOnjwJhUIhzfGlC0xKERERGQkNWClF2uRyOfz8/HDgwAEMGTJEWn7gwAEMHjy4ym0CAgLw3XffaS3bv3+/9AOaiIioKr169YK3tzdee+01FBYWYuTIkZg/f760ftGiRXB0dMSyZctw48YNNGnSBJ07d6526Fld1KWP/v37w9zcHD///DOCg4OxZ88ePHz4UOt7s5y3tzc6dOiAqKgorFq1Cl27dsW+ffuwcOFC6QEhL730Evbt24cuXbpI29nZ2eHkyZP4+OOPsXjxYty6dQt2dnbo0KEDli9fLg31q28qlQoHDhzAlClT4O/vDzs7O8yYMUOrujkvLw+XL1+u9GTFqKgodOvWDW3bVn0T6Mcff8SSJUtQWFiIjh07Yvfu3ZWGGsbExGDUqFFac43VN5kwkrECZvLmz25ERDViZlK78eZEVL2CglS99aWyblXv+8x6fL3e90n6tW3bNoSGhmLdunUICAjAv/71L3z55Ze4ePEiPDw8MGfOHNy9exebNm0CAKSkpKB9+/aYOHEiJkyYgBMnTmDSpEmIiYmp8dP3srOzoVKpkJWVBVtbW10eHhGR0SooKEBKSgq8vLyqnEi7IQkLC0NmZia+/fZbQ4dSJ2vWrMHu3buxb59hnozYED148AC+vr44c+ZMtU/5e9pnpKa/JVgpRUREZCSM5D4RGZkRI0bg4cOHWLhwIdRqNdq3b48ffvgBHh4eAMoe3Zya+r/kqZeXF3744QdMnz4dq1evhqurK1atWlXjhBQREdGL5u2330ZGRgZycnK0nu5HdZeSkoI1a9ZUm5CqL6yUImqAWClFVH/0WSllbVn/X/qP81LqfZ/U8LFSioiIlVJEz8JKKSIiIiIiIiJ6LtHR0YYOgRopJqWIiIiMhOBE50RERETUiJgYOgAiIiIiIiIiImp8WClFRERkJDTGMc0jERERPcFIpmEmMjr18dlgUoqIiMhI8EcvERGR8TA3NwcA5OXlQalUGjgaIuOTl5cH4H+flbpgUoqIiIiIiIioAlNTUzRp0gT3798HAFhaWkImkxk4KiLDE0IgLy8P9+/fR5MmTWBqWvenvzMpRUREZCQ40TkREZFxcXZ2BgApMUVE/9OkSRPpM1JXTEoRERERERERVUEmk8HFxQWOjo4oLi42dDhERsPc3Py5KqTKMSlFRERkJDinFBERkXEyNTWtl3+AE5E2JqWIiIiMBJNSRERERNSYmBg6ACIiIiIiIiIianxYKUVERGQkWCdFRERERI0Jk1JEREREpKV8KGl2draBIyEiIqIXUflviGdNT2E0SamSoruGDoGeobCwEMuWLcOcOXOgUCgMHQ7RC42fJ6oKvwvJWOTk5AAA3N3dDRwJERERvchycnKgUqmqXS8TnFWVaig7OxsqlQpZWVmwtbU1dDhELzR+nojImGk0Gty7dw82NjaQyWQ66SM7Oxvu7u64ffs2/w4aAM+/4fEaGBbPv2Hx/Buerq+BEAI5OTlwdXWFiUn105kbTaUUERERERkHExMTuLm56aUvW1tb/oPEgHj+DY/XwLB4/g2L59/wdHkNnlYhVY5P3yMiIiIiIiIiIr1jUoqIiIiIiIiIiPSOSSmqMYVCgYiICE7KTFQP+HkiosaOfwcNi+ff8HgNDIvn37B4/g3PWK4BJzonIiIiIiIiIiK9Y6UUERERERERERHpHZNSRERERERERESkd0xKUY1ER0ejSZMmhg6DiIiIiIiIiBoIJqUambCwMMhkskqva9euGTo0ohdSVZ+nJ19hYWGGDpGIyCDWrFkDLy8vWFhYwM/PD8eOHXtq+yNHjsDPzw8WFhZo2bIl1q1bp6dIG67aXIOdO3ciKCgIzZo1g62tLQICArBv3z49Rtvw1PYzUO7XX3+FmZkZXnnlFd0G2AjU9hoUFhZi3rx58PDwgEKhQKtWrbBhwwY9Rdvw1Pb8b9myBR07doSlpSVcXFwwduxYPHz4UE/RNixHjx7FwIED4erqCplMhm+//faZ2xjqe5hJqUaob9++UKvVWi8vLy9Dh0X0Qnryc7Ry5UrY2tpqLfvnP/+p1b64uNhAkRIR6c+2bdswbdo0zJs3D4mJiejRowf69euH1NTUKtunpKSgf//+6NGjBxITEzF37lyEh4fjm2++0XPkDUdtr8HRo0cRFBSEH374AQkJCejZsycGDhyIxMREPUfeMNT2/JfLysrC6NGj0atXLz1F2nDV5RoMHz4cBw8eRFRUFC5fvoyYmBj4+vrqMeqGo7bnPy4uDqNHj8a4ceNw8eJF7NixA/Hx8Rg/fryeI28YcnNz0bFjR3zxxRc1am/Q72FBjcqYMWPE4MGDKy1fsWKFaN++vbC0tBRubm7inXfeETk5OdL6jRs3CpVKJb1PSkoSr7/+urC2thY2Njaic+fOIj4+Xlr/66+/ih49eggLCwvh5uYm3n33XfH48WNdHhqRwVX8nKSkpAgAYtu2bSIwMFAoFAqxYcMGERERITp27Ki17WeffSY8PDy0lm3YsEH4+voKhUIh2rRpI1avXq37gyAiqgevvvqqmDRpktYyX19fMXv27Crbz5o1S/j6+motmzhxoujatavOYmzoansNqtKuXTuxYMGC+g6tUajr+R8xYoT4+9//XuVvBaqd2l6DH3/8UahUKvHw4UN9hNfg1fb8L1++XLRs2VJr2apVq4Sbm5vOYmwsAIhdu3Y9tY0hv4dZKUUAABMTE6xatQoXLlzAV199hV9++QWzZs2qtv2oUaPg5uaG+Ph4JCQkYPbs2TA3NwcAnD9/HsHBwRg6dCjOnTuHbdu2IS4uDlOnTtXX4RAZlQ8++ADh4eG4dOkSgoODa7TNl19+iXnz5mHJkiW4dOkSli5dig8//BBfffWVjqMlIno+RUVFSEhIQJ8+fbSW9+nTB8ePH69ymxMnTlRqHxwcjDNnzrDCtA7qcg0q0mg0yMnJQdOmTXURYoNW1/O/ceNGXL9+HREREboOscGryzXYs2cP/P398cknn6B58+bw8fHBzJkzkZ+fr4+QG5S6nP9u3brhzp07+OGHHyCEwO+//46vv/4aAwYM0EfIjZ4hv4fNdLp3Mkrff/89rK2tpff9+vXDjh07pPdeXl5YtGgR3nnnHaxZs6bKfaSmpuL999+Xylm9vb2ldcuXL0dISAimTZsmrVu1ahUCAwOxdu1aWFhY6OCoiIzXtGnTMHTo0Fpts2jRIqxYsULazsvLC8nJyVi/fj3GjBmjizCJiOpFeno6SktL4eTkpLXcyckJaWlpVW6TlpZWZfuSkhKkp6fDxcVFZ/E2RHW5BhWtWLECubm5GD58uC5CbNDqcv6vXr2K2bNn49ixYzAz4z/RnlddrsGNGzcQFxcHCwsL7Nq1C+np6Zg8eTIePXrEeaVqqS7nv1u3btiyZQtGjBiBgoIClJSUYNCgQfj888/1EXKjZ8jvYf7Fa4R69uyJtWvXSu+trKxw6NAhLF26FMnJycjOzkZJSQkKCgqQm5sLKyurSvuYMWMGxo8fj82bN6N3794YNmwYWrVqBQBISEjAtWvXsGXLFqm9EAIajQYpKSlo27at7g+SyIj4+/vXqv2DBw9w+/ZtjBs3DhMmTJCWl5SUQKVS1Xd4REQ6IZPJtN4LISote1b7qpZTzdX2GpSLiYnB/PnzsXv3bjg6OuoqvAavpue/tLQUISEhWLBgAXx8fPQVXqNQm8+ARqOBTCbDli1bpN9bkZGRePPNN7F69WoolUqdx9vQ1Ob8JycnIzw8HB999BGCg4OhVqvx/vvvY9KkSYiKitJHuI2eob6HmZRqhKysrNC6dWvp/a1bt9C/f39MmjQJixYtQtOmTREXF4dx48ZVW6o3f/58hISEYO/evfjxxx8RERGB2NhYDBkyBBqNBhMnTkR4eHil7Vq0aKGz4yIyVhUTuyYmJtIf+XJPftY0Gg2AsiF8Xbp00WpnamqqoyiJiOqHg4MDTE1NK90Nv3//fqW7sOWcnZ2rbG9mZgZ7e3udxdpQ1eUalNu2bRvGjRuHHTt2oHfv3roMs8Gq7fnPycnBmTNnkJiYKE13odFoIISAmZkZ9u/fjz/+8Y96ib2hqMtnwMXFBc2bN9e6Adi2bVsIIXDnzh2tkSH0dHU5/8uWLUP37t3x/vvvAwBefvllWFlZoUePHli8eDErZnXMkN/DnFOKcObMGZSUlGDFihXo2rUrfHx8cO/evWdu5+Pjg+nTp2P//v0YOnQoNm7cCADo3LkzLl68iNatW1d6yeVyXR8OkdFr1qwZ0tLStBJTSUlJ0n87OTmhefPmuHHjRqXPEJ+USUTGTi6Xw8/PDwcOHNBafuDAAXTr1q3KbQICAiq1379/P/z9/aU5K6nm6nINgLIKqbCwMGzdupXzuDyH2p5/W1tbnD9/HklJSdJr0qRJaNOmDZKSkirdoKJnq8tnoHv37rh37x4eP34sLbty5QpMTEzg5uam03gbmrqc/7y8PJiYaKcnym/GVryZS/XPoN/DOp9KnYxKVU/fS0xMFADEypUrxfXr18WmTZtE8+bNBQCRkZEhhNB+qlheXp6YMmWKOHTokLh586aIi4sTrVq1ErNmzRJCCPHbb78JpVIpJk+eLBITE8WVK1fE7t27xdSpU/V4pET6V93T9xITE7XaJScnC5lMJj7++GNx7do18cUXXwg7Ozutp+99+eWXQqlUipUrV4rLly+Lc+fOiQ0bNogVK1bo52CIiJ5DbGysMDc3F1FRUSI5OVlMmzZNWFlZiZs3bwohhJg9e7YIDQ2V2t+4cUNYWlqK6dOni+TkZBEVFSXMzc3F119/bahDeOHV9hps3bpVmJmZidWrVwu1Wi29MjMzDXUIL7Tanv+K+PS951fba5CTkyPc3NzEm2++KS5evCiOHDkivL29xfjx4w11CC+02p7/jRs3CjMzM7FmzRpx/fp1ERcXJ/z9/cWrr75qqEN4oeXk5IjExETp3/qRkZEiMTFR3Lp1SwhhXN/DTEo1MlUlpYQQIjIyUri4uAilUimCg4PFpk2bqk1KFRYWipEjRwp3d3chl8uFq6urmDp1qsjPz5f2d/r0aREUFCSsra2FlZWVePnll8WSJUv0cIREhlPTpJQQQqxdu1a4u7sLKysrMXr0aLFkyRKtpJQQQmzZskW88sorQi6XCzs7O/Haa6+JnTt36vYgiIjqyerVq4WHh4eQy+Wic+fO4siRI9K6MWPGiMDAQK32hw8fFp06dRJyuVx4enqKtWvX6jnihqc21yAwMFAAqPQaM2aM/gNvIGr7GXgSk1L1o7bX4NKlS6J3795CqVQKNzc3MWPGDJGXl6fnqBuO2p7/VatWiXbt2gmlUilcXFzEqFGjxJ07d/QcdcNw6NChp/5NN6bvYZkQrIUjIiIiIiIiIiL94pxSRERERERERESkd0xKERERERERERGR3jEpRUREREREREREesekFBERERERERER6R2TUkREREREREREpHdMShERERERERERkd4xKUVERERERERERHrHpBQREREREREREekdk1JERERERERERKR3TEoREREREZHBREdHo0mTJoYOo848PT2xcuXKp7aZP38+XnnlFb3EY2x++eUX+Pr6QqPR6KU/Y7kedelDJpPh22+/fa5+w8LC8Kc//em59lGVP/zhD9i5c2e975eISSkiIiIiInouYWFhkMlklV7Xrl0zdGiIjo7WisnFxQXDhw9HSkpKvew/Pj4eb7/9tvS+qsTCzJkzcfDgwXrprzoVj9PJyQkDBw7ExYsXa72f+kwSzpo1C/PmzYOJiUmVcTbU6/EiOXr0KAYOHAhXV9dqE2MffvghZs+erbfkIjUeTEoREREREdFz69u3L9RqtdbLy8vL0GEBAGxtbaFWq3Hv3j1s3boVSUlJGDRoEEpLS597382aNYOlpeVT21hbW8Pe3v65+3qWJ49z7969yM3NxYABA1BUVKTzvqty/PhxXL16FcOGDas2zoZ8PV4Uubm56NixI7744otq2wwYMABZWVnYt2+fHiOjxoBJKSIiIiIiem4KhQLOzs5aL1NTU0RGRqJDhw6wsrKCu7s7Jk+ejMePH1e7n99++w09e/aEjY0NbG1t4efnhzNnzkjrjx8/jtdeew1KpRLu7u4IDw9Hbm7uU2OTyWRwdnaGi4sLevbsiYiICFy4cEGq5Fq7di1atWoFuVyONm3aYPPmzVrbz58/Hy1atIBCoYCrqyvCw8OldU8OF/P09AQADBkyBDKZTHr/5FCuffv2wcLCApmZmVp9hIeHIzAwsN6O09/fH9OnT8etW7dw+fJlqc3Trsfhw4cxduxYZGVlSZVM8+fPBwAUFRVh1qxZaN68OaysrNClSxccPnz4qfHExsaiT58+sLCwqDbOhnw9nhQfH4+goCA4ODhApVIhMDAQZ8+erdROrVajX79+UCqV8PLywo4dO7TW3717FyNGjICdnR3s7e0xePBg3Lx5s8ZxVKVfv35YvHgxhg4dWm0bU1NT9O/fHzExMc/VF1FFTEoREREREZHOmJiYYNWqVbhw4QK++uor/PLLL5g1a1a17UeNGgU3NzfEx8cjISEBs2fPhrm5OQDg/PnzCA4OxtChQ3Hu3Dls27YNcXFxmDp1aq1iUiqVAIDi4mLs2rULf/vb3/Dee+/hwoULmDhxIsaOHYtDhw4BAL7++mt89tlnWL9+Pa5evYpvv/0WHTp0qHK/8fHxAICNGzdCrVZL75/Uu3dvNGnSBN988420rLS0FNu3b8eoUaPq7TgzMzOxdetWAJDOH/D069GtWzesXLlSqmRSq9WYOXMmAGDs2LH49ddfERsbi3PnzmHYsGHo27cvrl69Wm0MR48ehb+//zNjbQzXIycnB2PGjMGxY8dw8uRJeHt7o3///sjJydFq9+GHH+LPf/4zfvvtN7z11lv4y1/+gkuXLgEA8vLy0LNnT1hbW+Po0aOIi4uDtbU1+vbtW201XPlwyfrw6quv4tixY/WyLyKJICIiIiIieg5jxowRpqamwsrKSnq9+eabVbbdvn27sLe3l95v3LhRqFQq6b2NjY2Ijo6uctvQ0FDx9ttvay07duyYMDExEfn5+VVuU3H/t2/fFl27dhVubm6isLBQdOvWTUyYMEFrm2HDhon+/fsLIYRYsWKF8PHxEUVFRVXu38PDQ3z22WfSewBi165dWm0iIiJEx44dpffh4eHij3/8o/R+3759Qi6Xi0ePHj3XcQIQVlZWwtLSUgAQAMSgQYOqbF/uWddDCCGuXbsmZDKZuHv3rtbyXr16iTlz5lS7b5VKJTZt2lQpzsZwPSr2UVFJSYmwsbER3333nVaskyZN0mrXpUsX8c477wghhIiKihJt2rQRGo1GWl9YWCiUSqXYt2+fEKLsszh48GBp/c6dO0WbNm2qjaOiqs5Xud27dwsTExNRWlpa4/0RPQsrpYiIiIiI6Ln17NkTSUlJ0mvVqlUAgEOHDiEoKAjNmzeHjY0NRo8ejYcPH1Y79GnGjBkYP348evfujY8//hjXr1+X1iUkJCA6OhrW1tbSKzg4GBqN5qkTZWdlZcHa2loaslZUVISdO3dCLpfj0qVL6N69u1b77t27S9Upw4YNQ35+Plq2bIkJEyZg165dKCkpea5zNWrUKBw+fBj37t0DAGzZsgX9+/eHnZ3dcx2njY0NkpKSkJCQgHXr1qFVq1ZYt26dVpvaXg8AOHv2LIQQ8PHx0YrpyJEjWtenovz8/EpD94DGcz2edP/+fUyaNAk+Pj5QqVRQqVR4/PgxUlNTtdoFBARUel9+7AkJCbh27RpsbGykOJo2bYqCgoJqr8OQIUPwn//8p1bnozpKpRIajQaFhYX1sj8iADAzdABERERERPTis7KyQuvWrbWW3bp1C/3798ekSZOwaNEiNG3aFHFxcRg3bhyKi4ur3M/8+fMREhKCvXv34scff0RERARiY2MxZMgQaDQaTJw4UWsOoXItWrSoNjYbGxucPXsWJiYmcHJygpWVldb6isObhBDSMnd3d1y+fBkHDhzAzz//jMmTJ2P58uU4cuSI1rC42nj11VfRqlUrxMbG4p133sGuXbuwceNGaX1dj9PExES6Br6+vkhLS8OIESNw9OhRAHW7HuXxmJqaIiEhAaamplrrrK2tq93OwcEBGRkZlZY3luvxpLCwMDx48AArV66Eh4cHFAoFAgICajQJffmxazQa+Pn5YcuWLZXaNGvWrEZxPI9Hjx7B0tJSGm5JVB+YlCIiIiIiIp04c+YMSkpKsGLFCpiYlA3S2L59+zO38/HxgY+PD6ZPn46//OUv2LhxI4YMGYLOnTvj4sWLlZJfz/Jksqaitm3bIi4uDqNHj5aWHT9+HG3btpXeK5VKDBo0CIMGDcKUKVPg6+uL8+fPo3PnzpX2Z25uXqOnyIWEhGDLli1wc3ODiYkJBgwYIK2r63FWNH36dERGRmLXrl0YMmRIja6HXC6vFH+nTp1QWlqK+/fvo0ePHjXuv1OnTkhOTq60vDFej2PHjmHNmjXo378/AOD27dtIT0+v1O7kyZNax37y5El06tRJimPbtm1wdHSEra1tnWOpqwsXLlR5jomeB4fvERERERGRTrRq1QolJSX4/PPPcePGDWzevLnScLIn5efnY+rUqTh8+DBu3bqFX3/9FfHx8VJC4oMPPsCJEycwZcoUJCUl4erVq9izZw/efffdOsf4/vvvIzo6GuvWrcPVq1cRGRmJnTt3ShN8R0dHIyoqChcuXJCOQalUwsPDo8r9eXp64uDBg0hLS6uySqjcqFGjcPbsWSxZsgRvvvmm1jC3+jpOW1tbjB8/HhERERBC1Oh6eHp64vHjxzh48CDS09ORl5cHHx8fjBo1CqNHj8bOnTuRkpKC+Ph4/OMf/8APP/xQbf/BwcGIi4urVcwN9Xq0bt0amzdvxqVLl3Dq1CmMGjWqyoqjHTt2YMOGDbhy5QoiIiJw+vRpaUL1UaNGwcHBAYMHD8axY8eQkpKCI0eO4G9/+xvu3LlTZb+7du2Cr6/vU2N7/PixNOwWAFJSUpCUlFRpaOGxY8fQp0+fGh8zUY0YdkorIiIiIiJ60VWcXPlJkZGRwsXFRSiVShEcHCw2bdokAIiMjAwhhPbE14WFhWLkyJHC3d1dyOVy4erqKqZOnao1mfTp06dFUFCQsLa2FlZWVuLll18WS5YsqTa2qiburmjNmjWiZcuWwtzcXPj4+GhNzr1r1y7RpUsXYWtrK6ysrETXrl3Fzz//LK2vOLH2nj17ROvWrYWZmZnw8PAQQlQ/6fUf/vAHAUD88ssvldbV13HeunVLmJmZiW3btgkhnn09hBBi0qRJwt7eXgAQERERQgghioqKxEcffSQ8PT2Fubm5cHZ2FkOGDBHnzp2rNqZHjx4JpVIp/vOf/zwzzic1hOtRsY+zZ88Kf39/oVAohLe3t9ixY0eVk7KvXr1aBAUFCYVCITw8PERMTIzWftVqtRg9erRwcHAQCoVCtGzZUkyYMEFkZWUJISp/FssnwH+aQ4cOSRPjP/kaM2aM1ObOnTvC3Nxc3L59+6n7IqotmRBCGCYdRkRERERERA3ZrFmzkJWVhfXr1xs6FHoO77//PrKysvCvf/3L0KFQA8Phe0RERERERKQT8+bNg4eHR43mdSLj5ejoiEWLFhk6DGqAWClFRERERERERER6x0opIiIiIiIiIiLSOyaliIiIiIiIiIhI75iUIiIiIiIiIiIivWNSioiIiIiIiIiI9I5JKSIiIiIiIiIi0jsmpYiIiIiIiIiISO+YlCIiIiIiIiIiIr1jUoqIiIiIiIiIiPSOSSkiIiIiIiIiItK7/wfLMw3reWGGSgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 1200x500 with 3 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))\n",
+ "ax[0] = get_cf_mat(y, y_pred, ax[0])\n",
+ "ax[1] = RocCurveDisplay.from_estimator(model, X, y, ax=ax[1])\n",
+ "plt.tight_layout()\n",
+ "plt.savefig(\"../images/validation_res_250k.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "20dbfc80-1ce8-4795-9956-d480e141dce9",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/visualizations.ipynb b/notebooks/visualizations.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..e2985db09a257b6d12b9576505326a6e1cfcaaf5
--- /dev/null
+++ b/notebooks/visualizations.ipynb
@@ -0,0 +1,296 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "eed66840",
+ "metadata": {},
+ "source": [
+ "# KFold cross validation behaviour"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "ae19a511",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import warnings\n",
+ "warnings.filterwarnings('ignore')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "771fbb59",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from sklearn.model_selection import (\n",
+ " TimeSeriesSplit,\n",
+ " KFold,\n",
+ " ShuffleSplit,\n",
+ " StratifiedKFold,\n",
+ " GroupShuffleSplit,\n",
+ " GroupKFold,\n",
+ " StratifiedShuffleSplit,\n",
+ " StratifiedGroupKFold,\n",
+ ")\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "from matplotlib.patches import Patch\n",
+ "\n",
+ "rng = np.random.RandomState(1338)\n",
+ "cmap_data = plt.cm.Paired\n",
+ "cmap_cv = plt.cm.coolwarm\n",
+ "n_splits = 5\n",
+ "\n",
+ "\n",
+ "def visualize_groups(classes, groups, name, ax):\n",
+ " # Visualize dataset groups\n",
+ " ax.scatter(\n",
+ " range(len(groups)),\n",
+ " [0.5] * len(groups),\n",
+ " c=groups,\n",
+ " marker=\"_\",\n",
+ " lw=50,\n",
+ " cmap=cmap_data,\n",
+ " )\n",
+ " ax.scatter(\n",
+ " range(len(groups)),\n",
+ " [3.5] * len(groups),\n",
+ " c=classes,\n",
+ " marker=\"_\",\n",
+ " lw=50,\n",
+ " cmap=cmap_data,\n",
+ " )\n",
+ " ax.set(\n",
+ " ylim=[-1, 5],\n",
+ " yticks=[0.5, 3.5],\n",
+ " yticklabels=[\"Data\\ngroup\", \"Data\\nclass\"],\n",
+ " xlabel=\"Sample index\",\n",
+ " )\n",
+ "\n",
+ "def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10):\n",
+ " \"\"\"Create a sample plot for indices of a cross-validation object.\"\"\"\n",
+ "\n",
+ " # Generate the training/testing visualizations for each CV split\n",
+ " for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=group)):\n",
+ " # Fill in indices with the training/test groups\n",
+ " indices = np.array([np.nan] * len(X))\n",
+ " indices[tt] = 1\n",
+ " indices[tr] = 0\n",
+ "\n",
+ " # Visualize the results\n",
+ " ax.scatter(\n",
+ " range(len(indices)),\n",
+ " [ii + 0.5] * len(indices),\n",
+ " c=indices,\n",
+ " marker=\"_\",\n",
+ " lw=lw,\n",
+ " cmap=cmap_cv,\n",
+ " vmin=-0.2,\n",
+ " vmax=1.2,\n",
+ " )\n",
+ "\n",
+ " # Plot the data classes and groups at the end\n",
+ " ax.scatter(\n",
+ " range(len(X)), [ii + 1.5] * len(X), c=y, marker=\"_\", lw=lw, cmap=cmap_data\n",
+ " )\n",
+ "\n",
+ " ax.scatter(\n",
+ " range(len(X)), [ii + 2.5] * len(X), c=group, marker=\"_\", lw=lw, cmap=cmap_data\n",
+ " )\n",
+ "\n",
+ " # Formatting\n",
+ " yticklabels = list(range(n_splits)) + [\"class\", \"group\"]\n",
+ " ax.set(\n",
+ " yticks=np.arange(n_splits + 2) + 0.5,\n",
+ " yticklabels=yticklabels,\n",
+ " xlabel=\"Sample index\",\n",
+ " ylabel=\"CV iteration\",\n",
+ " ylim=[n_splits + 2.2, -0.2],\n",
+ " xlim=[0, np.max(group)],\n",
+ " )\n",
+ " # ax.set_title(\"{}\".format(type(cv).__name__), fontsize=15)\n",
+ " return ax"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "e4e065ff",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "DATA_DIR = \"../project_dataset\"\n",
+ "TRAIN_DIR = f\"{DATA_DIR}/partial_dataset_train\"\n",
+ "\n",
+ "X = pd.read_csv(f\"{TRAIN_DIR}/features.csv\", index_col=0)[:30000]\n",
+ "y = pd.read_csv(f\"{TRAIN_DIR}/labels.csv\", index_col=0)[:30000]\n",
+ "cl_df = pd.read_csv(f\"{TRAIN_DIR}/complete_labels.csv\", index_col=0)[:30000]\n",
+ "\n",
+ "if \"group_id\" in cl_df:\n",
+ " cl_df.drop(\"group_id\", axis=1)\n",
+ "\n",
+ "cl_df[\"group_id\"] = cl_df.astype(bool).groupby(cl_df.columns.tolist(), sort=False).ngroup() + 1\n",
+ "min_ = cl_df[\"group_id\"].min()\n",
+ "max_ = cl_df[\"group_id\"].max()\n",
+ "\n",
+ "def f(r):\n",
+ " if r[\"label\"] == False:\n",
+ " r[\"group_id\"] = np.random.randint(min_, max_, size=1)[0]\n",
+ " return r[\"group_id\"]\n",
+ "\n",
+ "group_ids = cl_df[[\"label\", \"group_id\"]].apply(f, axis=1)\n",
+ "\n",
+ "X = X.values\n",
+ "y = y.values.squeeze()\n",
+ "groups = group_ids.to_numpy()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5c7c01ad",
+ "metadata": {},
+ "source": [
+ "## Cross Validation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "79ee7631",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 1500x500 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15, 5))\n",
+ "cv = StratifiedGroupKFold(n_splits)\n",
+ "visualize_groups(y, groups, \"no groups\", ax[0])\n",
+ "plot_cv_indices(cv, X, y, groups, ax[1], n_splits)\n",
+ "# plt.tight_layout()\n",
+ "# plt.savefig(\"../images/cv_kfold.png\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "053733ce-7ee3-4fa6-9400-1763c73f1f40",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 0, 1, 2, ..., 29997, 29998, 29999], dtype=int64)"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "z = np.array([0, 1, 1, 1, 2, 3, 0 , 0])\n",
+ "# np.random.shuffle()\n",
+ "# np.where(z == 0)\n",
+ "ppp = np.where(y == False)[0]\n",
+ "np.random.shuffle(ppp)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "id": "5ac483bd-3001-4da9-a012-1eff05dfda96",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1500x500 with 4 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# np.random.seed(0)\n",
+ "from imblearn.under_sampling import *\n",
+ "\n",
+ "f_id = np.where(y == False)[0]\n",
+ "np.random.shuffle(f_id)\n",
+ "f_id = f_id[:1000]\n",
+ "t_id = np.where(y == True)[0]\n",
+ "np.random.shuffle(t_id)\n",
+ "t_id = t_id[:30]\n",
+ "\n",
+ "names = [[\"Original Dataset\", \"Near Miss\"],\n",
+ " [\"One Sided Selection\", \"Edited Nearest Neighbours\"]]\n",
+ "one = NearMiss(sampling_strategy=1/15, n_jobs=-1)\n",
+ "two = OneSidedSelection(sampling_strategy=\"majority\", n_neighbors=3, n_seeds_S=50, n_jobs=-1)\n",
+ "three = EditedNearestNeighbours(sampling_strategy=\"majority\", n_neighbors=11, n_jobs=-1)\n",
+ "\n",
+ "X_rand = np.vstack((X[f_id], X[t_id]))\n",
+ "y_rand = np.hstack((y[f_id], y[t_id]))\n",
+ "\n",
+ "X_y_p = [[(X_rand, y_rand), one.fit_resample(X_rand, y_rand)],\n",
+ " [two.fit_resample(X_rand, y_rand), three.fit_resample(X_rand, y_rand)]]\n",
+ "\n",
+ "fig, ax = plt.subplots(nrows=1, ncols=4, figsize=(15, 5))\n",
+ "\n",
+ "for i in range(2):\n",
+ " for j in range(2):\n",
+ " tmp1, tmp2 = X_y_p[i][j]\n",
+ " \n",
+ " for label in (0, 1):\n",
+ " id_ = np.where(tmp2 == label)[0]\n",
+ " ax[(i * 2) + j].scatter(tmp1[id_, 0], tmp1[id_, 1], s=10, alpha=0.5, label=str(label))\n",
+ " ax[(i * 2) + j].legend([\"false\", \"true\"])\n",
+ " ax[(i * 2) + j].set_title(names[i][j] + f\" (n={len(tmp2)})\")\n",
+ " # ax[i, j].set_yticks([0, 0.05, 0.1, 0.15])\n",
+ " # ax[i, j].set_xticks([0, 0.1, 0.2])\n",
+ " \n",
+ " \n",
+ "plt.tight_layout()\n",
+ "plt.savefig(\"../images/imb_scatters.png\")\n",
+ "plt.show() "
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.15"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}